{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/nyandwi/machine_learning_complete/blob/main/6_classical_machine_learning_with_scikit-learn/2_linear_models_for_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*This notebook was created by [Jean de Dieu Nyandwi](https://twitter.com/jeande_d) for the love of machine learning community. For any feedback, errors or suggestion, he can be reached on email (johnjw7084 at gmail dot com), [Twitter](https://twitter.com/jeande_d), or [LinkedIn](https://linkedin.com/in/nyandwi).*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5qKZ9Wil-4_n"
   },
   "source": [
    "<a name='0'></a>\n",
    "# Linear Models for Classification\n",
    "\n",
    "In the previous lab, we learned about regression where the goal was to predict the continous value such as the price of house given the information about the house. \n",
    "\n",
    "In this lab, we will learn about classification where the task is to predict the class or category. Both regression and classification are the main two types of supervised learning. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4a3ep0qkBQNt"
   },
   "source": [
    "As always, we are going to approach our problem following a typical Machine Learning workflow. \n",
    "\n",
    "* [1. Problem formulation](#1)\n",
    "* [2. Finding data](#2)\n",
    "* [3. Exploring insights in data or EDA](#3)\n",
    "* [4. Data preprocessing](#4)\n",
    "* [5. Choosing and training a model](#5)\n",
    "* [6. Evaluating a model](#6)\n",
    "\n",
    "\n",
    "By being systematic and keeping things organized, it will help you to reproduce some parts of the project or reuse them into other problems. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r3n3Y80-FU-S"
   },
   "source": [
    "<a name='1'></a>\n",
    "\n",
    "## 1. Problem Formulation\n",
    "\n",
    "Let's say you have an idea of a revolutionary mobile phone and you want to establish a start up, but you know little about the price of the mobile phones. You are interested in learning that! \n",
    "\n",
    "Fortunately, there is this mobile dataset [on Kaggle](https://www.kaggle.com/iabhishekofficial/mobile-price-classification?select=train.csv) that you can use to learn about the price ranges of mobiles based on their features such as wifi & bluetooth supports etc...\n",
    "\n",
    "So, to make it simple, you have a dataset containing the features of mobiles and the problem is to predict the price range, not the exact price. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hKZtJ5aQR0U1"
   },
   "source": [
    "<a name='2'></a>\n",
    "\n",
    "## 2. Finding the Data\n",
    "\n",
    "The data that we are going to use is found on Kaggle. \n",
    "\n",
    "Here are the details of the features. It is 21 features. The target feature is `price range` and it has four price ranges: `0(low cost), 1(medium cost), 2(high cost) and 3(very high cost).`\n",
    "\n",
    "* **batter_power**: Total energy a battery can store in one time measured in mAh\n",
    "* **blue**: Has bluetooth or not\n",
    "* **clock_speed**: speed at which microprocessor executes instructions\n",
    "* **dual_sim**: Has dual sim support or not\n",
    "* **fc**: Front Camera mega pixels\n",
    "* **four_g**: Has 4G or not\n",
    "* **int_memory**: Internal Memory in Gigabytes\n",
    "* **m_dep**: Mobile Depth in cm\n",
    "* **mobile_wt**: Weight of mobile phone\n",
    "* **n_cores**: Number of cores of processor\n",
    "* **pc**: Primary Camera mega pixels\n",
    "* **px_height**: Pixel Resolution Height\n",
    "* **px_width**: Pixel Resolution Width\n",
    "* **ram**: Random Access Memory in Mega Bytes\n",
    "* **sc_h**: Screen Height of mobile in cm\n",
    "* **sc_w**: Screen Width of mobile in cm\n",
    "* **talk_time**: longest time that a single battery charge will last when you are talking\n",
    "* **three_g**: Has 3G or not\n",
    "* **touch_screen**: Has touch screen or not\n",
    "* **wifi**: Has wifi or not\n",
    "* **price_range**: This is the target variable with value of 0(low cost), 1(medium cost), 2(high cost) and 3(very high cost).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2Q9nRA2cVAKh"
   },
   "source": [
    "Let's download the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "5KaJ3glhVHLw"
   },
   "outputs": [],
   "source": [
    "import urllib.request \n",
    "import pandas as pd\n",
    "\n",
    "train_data_path = 'https://raw.githubusercontent.com/nyandwi/public_datasets/master/mobile_price_train.csv'\n",
    "test_data_path = 'https://raw.githubusercontent.com/nyandwi/public_datasets/master/mobile_price_test.csv'\n",
    "\n",
    "\n",
    "def download_read_data(path):\n",
    "    \n",
    "\n",
    "    \"\"\"\n",
    "    Function to retrieve data from the data paths\n",
    "    And to read the data as a pandas dataframe\n",
    "  \n",
    "    To return the dataframe\n",
    "    \"\"\" \n",
    "    # Only retrieve the directory of the data\n",
    "\n",
    "    data_path =  urllib.request.urlretrieve(path)[0]\n",
    "    data = pd.read_csv(str(data_path))\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "qswbz_sZcI1G"
   },
   "outputs": [],
   "source": [
    "# Getting train data \n",
    "\n",
    "mobile_train = download_read_data(train_data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Y2yWysRUctjK",
    "outputId": "b137c6a2-6ed4-48e6-d284-878c9f8e7f43",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>battery_power</th>\n",
       "      <th>blue</th>\n",
       "      <th>clock_speed</th>\n",
       "      <th>dual_sim</th>\n",
       "      <th>fc</th>\n",
       "      <th>four_g</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>m_dep</th>\n",
       "      <th>mobile_wt</th>\n",
       "      <th>n_cores</th>\n",
       "      <th>...</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>sc_h</th>\n",
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       "      <th>talk_time</th>\n",
       "      <th>three_g</th>\n",
       "      <th>touch_screen</th>\n",
       "      <th>wifi</th>\n",
       "      <th>price_range</th>\n",
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       "      <td>9</td>\n",
       "      <td>7</td>\n",
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       "      <td>0.7</td>\n",
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       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>905</td>\n",
       "      <td>1988</td>\n",
       "      <td>2631</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "      <th>2</th>\n",
       "      <td>563</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0.9</td>\n",
       "      <td>145</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1263</td>\n",
       "      <td>1716</td>\n",
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       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>615</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.8</td>\n",
       "      <td>131</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1216</td>\n",
       "      <td>1786</td>\n",
       "      <td>2769</td>\n",
       "      <td>16</td>\n",
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       "      <td>11</td>\n",
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       "      <td>2</td>\n",
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       "      <th>4</th>\n",
       "      <td>1821</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>0.6</td>\n",
       "      <td>141</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1208</td>\n",
       "      <td>1212</td>\n",
       "      <td>1411</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  m_dep  \\\n",
       "0            842     0          2.2         0   1       0           7    0.6   \n",
       "1           1021     1          0.5         1   0       1          53    0.7   \n",
       "2            563     1          0.5         1   2       1          41    0.9   \n",
       "3            615     1          2.5         0   0       0          10    0.8   \n",
       "4           1821     1          1.2         0  13       1          44    0.6   \n",
       "\n",
       "   mobile_wt  n_cores  ...  px_height  px_width   ram  sc_h  sc_w  talk_time  \\\n",
       "0        188        2  ...         20       756  2549     9     7         19   \n",
       "1        136        3  ...        905      1988  2631    17     3          7   \n",
       "2        145        5  ...       1263      1716  2603    11     2          9   \n",
       "3        131        6  ...       1216      1786  2769    16     8         11   \n",
       "4        141        2  ...       1208      1212  1411     8     2         15   \n",
       "\n",
       "   three_g  touch_screen  wifi  price_range  \n",
       "0        0             0     1            1  \n",
       "1        1             1     0            2  \n",
       "2        1             1     0            2  \n",
       "3        1             0     0            2  \n",
       "4        1             1     0            1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobile_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "vPCHIzb7dRiY"
   },
   "outputs": [],
   "source": [
    "# Getting test data \n",
    "\n",
    "mobile_test = download_read_data(test_data_path)\n",
    "# mobile_test.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NwZxcNvaeDeu",
    "outputId": "07df8e2c-bb07-4a05-ffd9-7ddcc120b62e"
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>battery_power</th>\n",
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       "      <th>clock_speed</th>\n",
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       "      <td>1</td>\n",
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       "      <td>1632</td>\n",
       "      <td>3057</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>1512</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>0.1</td>\n",
       "      <td>145</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>336</td>\n",
       "      <td>670</td>\n",
       "      <td>869</td>\n",
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       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>1999</th>\n",
       "      <td>510</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>0.9</td>\n",
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       "      <td>6</td>\n",
       "      <td>...</td>\n",
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       "      <td>754</td>\n",
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       "      <td>19</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  \\\n",
       "1995            794     1          0.5         1   0       1           2   \n",
       "1996           1965     1          2.6         1   0       0          39   \n",
       "1997           1911     0          0.9         1   1       1          36   \n",
       "1998           1512     0          0.9         0   4       1          46   \n",
       "1999            510     1          2.0         1   5       1          45   \n",
       "\n",
       "      m_dep  mobile_wt  n_cores  ...  px_height  px_width   ram  sc_h  sc_w  \\\n",
       "1995    0.8        106        6  ...       1222      1890   668    13     4   \n",
       "1996    0.2        187        4  ...        915      1965  2032    11    10   \n",
       "1997    0.7        108        8  ...        868      1632  3057     9     1   \n",
       "1998    0.1        145        5  ...        336       670   869    18    10   \n",
       "1999    0.9        168        6  ...        483       754  3919    19     4   \n",
       "\n",
       "      talk_time  three_g  touch_screen  wifi  price_range  \n",
       "1995         19        1             1     0            0  \n",
       "1996         16        1             1     1            2  \n",
       "1997          5        1             1     0            3  \n",
       "1998         19        1             1     1            0  \n",
       "1999          2        1             1     1            3  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at tail (last rows) of the data \n",
    "\n",
    "mobile_train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "D7KC0V8-eGp8",
    "outputId": "6d9fe5c6-4b11-4edd-8f97-322d675d5813"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   battery_power  2000 non-null   int64  \n",
      " 1   blue           2000 non-null   int64  \n",
      " 2   clock_speed    2000 non-null   float64\n",
      " 3   dual_sim       2000 non-null   int64  \n",
      " 4   fc             2000 non-null   int64  \n",
      " 5   four_g         2000 non-null   int64  \n",
      " 6   int_memory     2000 non-null   int64  \n",
      " 7   m_dep          2000 non-null   float64\n",
      " 8   mobile_wt      2000 non-null   int64  \n",
      " 9   n_cores        2000 non-null   int64  \n",
      " 10  pc             2000 non-null   int64  \n",
      " 11  px_height      2000 non-null   int64  \n",
      " 12  px_width       2000 non-null   int64  \n",
      " 13  ram            2000 non-null   int64  \n",
      " 14  sc_h           2000 non-null   int64  \n",
      " 15  sc_w           2000 non-null   int64  \n",
      " 16  talk_time      2000 non-null   int64  \n",
      " 17  three_g        2000 non-null   int64  \n",
      " 18  touch_screen   2000 non-null   int64  \n",
      " 19  wifi           2000 non-null   int64  \n",
      " 20  price_range    2000 non-null   int64  \n",
      "dtypes: float64(2), int64(19)\n",
      "memory usage: 328.2 KB\n"
     ]
    }
   ],
   "source": [
    "mobile_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iSyI9ESp2tWr",
    "outputId": "71b7651f-bce3-4a4a-fcab-377f1a529765"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of training data is: 2000 \n",
      "The size of testing data is: 1000\n"
     ]
    }
   ],
   "source": [
    "# Checking the number of data points/size of the data\n",
    "print('The size of training data is: {} \\nThe size of testing data is: {}'.format(len(mobile_train), len(mobile_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "svtnXnKb3QAa",
    "outputId": "9fa97a70-6d1e-4990-bb1b-bd78c8e080fe"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of features\n",
    "len(mobile_train.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M-EjqNlaeX9v"
   },
   "source": [
    "Now that we have our data, it's time to do exploratory analysis, trying to find the insights that can be helpful in our analysis & modelling. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kmocxwa2emxO"
   },
   "source": [
    "<a name='3'></a>\n",
    "\n",
    "## 3. Exploring Insights in Data or EDA\n",
    "\n",
    "In this part, we are going to learn more about the dataset. Let's start with the summary statistics, but before that, I will copy the training data in order to get it easily when we mess up down the road. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "FFekzvEw2IWI"
   },
   "outputs": [],
   "source": [
    "train_data = mobile_train.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AWp5agix1026"
   },
   "source": [
    "#### Checking summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "J0ecL5hz13YJ",
    "outputId": "e8f93b26-6ca8-4553-9cd6-56f31a98a9df"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>battery_power</th>\n",
       "      <th>blue</th>\n",
       "      <th>clock_speed</th>\n",
       "      <th>dual_sim</th>\n",
       "      <th>fc</th>\n",
       "      <th>four_g</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>m_dep</th>\n",
       "      <th>mobile_wt</th>\n",
       "      <th>n_cores</th>\n",
       "      <th>...</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>sc_h</th>\n",
       "      <th>sc_w</th>\n",
       "      <th>talk_time</th>\n",
       "      <th>three_g</th>\n",
       "      <th>touch_screen</th>\n",
       "      <th>wifi</th>\n",
       "      <th>price_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.0000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1238.518500</td>\n",
       "      <td>0.4950</td>\n",
       "      <td>1.522250</td>\n",
       "      <td>0.509500</td>\n",
       "      <td>4.309500</td>\n",
       "      <td>0.521500</td>\n",
       "      <td>32.046500</td>\n",
       "      <td>0.501750</td>\n",
       "      <td>140.249000</td>\n",
       "      <td>4.520500</td>\n",
       "      <td>...</td>\n",
       "      <td>645.108000</td>\n",
       "      <td>1251.515500</td>\n",
       "      <td>2124.213000</td>\n",
       "      <td>12.306500</td>\n",
       "      <td>5.767000</td>\n",
       "      <td>11.011000</td>\n",
       "      <td>0.761500</td>\n",
       "      <td>0.503000</td>\n",
       "      <td>0.507000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>439.418206</td>\n",
       "      <td>0.5001</td>\n",
       "      <td>0.816004</td>\n",
       "      <td>0.500035</td>\n",
       "      <td>4.341444</td>\n",
       "      <td>0.499662</td>\n",
       "      <td>18.145715</td>\n",
       "      <td>0.288416</td>\n",
       "      <td>35.399655</td>\n",
       "      <td>2.287837</td>\n",
       "      <td>...</td>\n",
       "      <td>443.780811</td>\n",
       "      <td>432.199447</td>\n",
       "      <td>1084.732044</td>\n",
       "      <td>4.213245</td>\n",
       "      <td>4.356398</td>\n",
       "      <td>5.463955</td>\n",
       "      <td>0.426273</td>\n",
       "      <td>0.500116</td>\n",
       "      <td>0.500076</td>\n",
       "      <td>1.118314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>501.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>851.750000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>282.750000</td>\n",
       "      <td>874.750000</td>\n",
       "      <td>1207.500000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1226.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1247.000000</td>\n",
       "      <td>2146.500000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1615.250000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>947.250000</td>\n",
       "      <td>1633.000000</td>\n",
       "      <td>3064.500000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1998.000000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>1998.000000</td>\n",
       "      <td>3998.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       battery_power       blue  clock_speed     dual_sim           fc  \\\n",
       "count    2000.000000  2000.0000  2000.000000  2000.000000  2000.000000   \n",
       "mean     1238.518500     0.4950     1.522250     0.509500     4.309500   \n",
       "std       439.418206     0.5001     0.816004     0.500035     4.341444   \n",
       "min       501.000000     0.0000     0.500000     0.000000     0.000000   \n",
       "25%       851.750000     0.0000     0.700000     0.000000     1.000000   \n",
       "50%      1226.000000     0.0000     1.500000     1.000000     3.000000   \n",
       "75%      1615.250000     1.0000     2.200000     1.000000     7.000000   \n",
       "max      1998.000000     1.0000     3.000000     1.000000    19.000000   \n",
       "\n",
       "            four_g   int_memory        m_dep    mobile_wt      n_cores  ...  \\\n",
       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000  ...   \n",
       "mean      0.521500    32.046500     0.501750   140.249000     4.520500  ...   \n",
       "std       0.499662    18.145715     0.288416    35.399655     2.287837  ...   \n",
       "min       0.000000     2.000000     0.100000    80.000000     1.000000  ...   \n",
       "25%       0.000000    16.000000     0.200000   109.000000     3.000000  ...   \n",
       "50%       1.000000    32.000000     0.500000   141.000000     4.000000  ...   \n",
       "75%       1.000000    48.000000     0.800000   170.000000     7.000000  ...   \n",
       "max       1.000000    64.000000     1.000000   200.000000     8.000000  ...   \n",
       "\n",
       "         px_height     px_width          ram         sc_h         sc_w  \\\n",
       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
       "mean    645.108000  1251.515500  2124.213000    12.306500     5.767000   \n",
       "std     443.780811   432.199447  1084.732044     4.213245     4.356398   \n",
       "min       0.000000   500.000000   256.000000     5.000000     0.000000   \n",
       "25%     282.750000   874.750000  1207.500000     9.000000     2.000000   \n",
       "50%     564.000000  1247.000000  2146.500000    12.000000     5.000000   \n",
       "75%     947.250000  1633.000000  3064.500000    16.000000     9.000000   \n",
       "max    1960.000000  1998.000000  3998.000000    19.000000    18.000000   \n",
       "\n",
       "         talk_time      three_g  touch_screen         wifi  price_range  \n",
       "count  2000.000000  2000.000000   2000.000000  2000.000000  2000.000000  \n",
       "mean     11.011000     0.761500      0.503000     0.507000     1.500000  \n",
       "std       5.463955     0.426273      0.500116     0.500076     1.118314  \n",
       "min       2.000000     0.000000      0.000000     0.000000     0.000000  \n",
       "25%       6.000000     1.000000      0.000000     0.000000     0.750000  \n",
       "50%      11.000000     1.000000      1.000000     1.000000     1.500000  \n",
       "75%      16.000000     1.000000      1.000000     1.000000     2.250000  \n",
       "max      20.000000     1.000000      1.000000     1.000000     3.000000  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobile_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UzVc6K3q4Chh"
   },
   "source": [
    "#### Checking missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PsHXcCSv4GsQ",
    "outputId": "4ee2c4ed-af07-43ab-b377-9af61dbf268e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "battery_power    0\n",
       "blue             0\n",
       "clock_speed      0\n",
       "dual_sim         0\n",
       "fc               0\n",
       "four_g           0\n",
       "int_memory       0\n",
       "m_dep            0\n",
       "mobile_wt        0\n",
       "n_cores          0\n",
       "pc               0\n",
       "px_height        0\n",
       "px_width         0\n",
       "ram              0\n",
       "sc_h             0\n",
       "sc_w             0\n",
       "talk_time        0\n",
       "three_g          0\n",
       "touch_screen     0\n",
       "wifi             0\n",
       "price_range      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobile_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UtS0F3UZ4Rev"
   },
   "source": [
    "We are lucky to not have missing values. If we had some, we would have to fill them with some strategies such as mean, remove them compleletly or leave them as they are. None of those 3 options is always the right choice for imputing missing values in all kinds of problems. it depends on the problem and the size of your dataset. Take an example, by removing a feature, you're loosing data. And by filling the values, you're adding noise or so. There is a trade off when it comes to imputing the missing values. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZW4ojtxA6MiQ"
   },
   "source": [
    "#### Checking Correlation Between Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YtMjP51d6PH0",
    "outputId": "e8f0f765-3317-4e71-a8c3-c0097034ae8d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "battery_power    0.200723\n",
       "blue             0.020573\n",
       "clock_speed     -0.006606\n",
       "dual_sim         0.017444\n",
       "fc               0.021998\n",
       "four_g           0.014772\n",
       "int_memory       0.044435\n",
       "m_dep            0.000853\n",
       "mobile_wt       -0.030302\n",
       "n_cores          0.004399\n",
       "pc               0.033599\n",
       "px_height        0.148858\n",
       "px_width         0.165818\n",
       "ram              0.917046\n",
       "sc_h             0.022986\n",
       "sc_w             0.038711\n",
       "talk_time        0.021859\n",
       "three_g          0.023611\n",
       "touch_screen    -0.030411\n",
       "wifi             0.018785\n",
       "price_range      1.000000\n",
       "Name: price_range, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation = mobile_train.corr()\n",
    "correlation['price_range']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kKC6D-aI6Z4s",
    "outputId": "918e0b0b-8917-4f6a-8a06-325fbd6be49e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing correlation\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(20,7))\n",
    "sns.heatmap(correlation, annot=True, cmap='PuBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Epv5lpw78qYM"
   },
   "source": [
    "Looking from the correlation map, the price ranges of the mobile are closely correlated with the `ram` or Random Access Memory by the correlation factor of `0.92`. So, that means the single determinant of how expensive the phone is going to be is its memory size and that makes sense even for many electronic devices. \n",
    "\n",
    "If this is your first time reading correlation, the correlation of 1 (or close to 1) means that the features contain the same information, and if you remove one of them (or remain with one of them), your model will not be affected. On the flip side, if the correlation is -1 (or close to -1), then it means that the features contain different information completely. \n",
    "\n",
    "There is another interesting insight to draw from the correlation. It seems that the feature `fc`(the megapixels of front camera) is correlated with `pc` which is the megapixels of the primary camera. \n",
    "\n",
    "That type of similarity is same across `three_g` and `four_g` and it makes sense. If you can pick up any smartphone that can support 3G network, there is a chance that it will also have 4G support. It is also the same across the size of the screen (`sc_h, sc_w`) and the pixel resolution (`px_height, px_width`). \n",
    "\n",
    "Let's take that insights from words to visualization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BVbDU-ohB1p4"
   },
   "source": [
    "#### More Data Exploration\n",
    "\n",
    "Again, let's see what we have in the price ranges. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qwS36NCpB9EK",
    "outputId": "21c9ca83-0c96-480d-c989-5b1b3c7ded3d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    500\n",
       "1    500\n",
       "2    500\n",
       "3    500\n",
       "Name: price_range, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobile_train['price_range'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "R756er01CTBT",
    "outputId": "c9d33775-9479-41da-db65-590fb34e96f0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jean/opt/miniconda3/envs/tensor/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Mobile Price Ranges')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(mobile_train['price_range'])\n",
    "plt.title('Mobile Price Ranges')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tyrzwb5cDbjM"
   },
   "source": [
    "This is cool, the price ranges are equal divided. We can confidently say that our data is balanced. Having unbalanced classes is a big issue because your model may learn to recognize the classes which dominate the data and fail to recognize the classes which are underrepresented. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also try to explore what's in the number of cores and their price ranges. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Cores')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "\n",
    "sns.countplot(data=mobile_train, x='n_cores')\n",
    "plt.title('Number of Cores')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Cores')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "\n",
    "sns.countplot(data=mobile_train, x='n_cores', hue='price_range')\n",
    "plt.title('Number of Cores')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also try to explore the distributions of features, starting with mobile weight and ram (random access memory)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='mobile_wt', ylabel='Count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "\n",
    "sns.histplot(data=mobile_train, x='mobile_wt', color='darkgreen')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='mobile_wt', ylabel='Count'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "\n",
    "sns.histplot(data=mobile_train, x='mobile_wt', palette='gist_rainbow', hue='price_range')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ram', ylabel='Count'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "\n",
    "sns.histplot(data=mobile_train, x='ram', palette='PRGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ram', ylabel='Count'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "\n",
    "sns.histplot(data=mobile_train, x='ram', palette='PRGn', hue='price_range')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, it seems that the phones that has over 2.5G of RAM are very expensive and that makes sense. The RAM is the big factor to determine the price of the phone. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='n_cores', ylabel='ram'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "\n",
    "sns.barplot(data=mobile_train, x='n_cores', y='ram')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`n_cores` is the number of cores possessed by a given processor. Plotting it with RAM, it doesn't show something remarkable. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LABHjn0QELmf"
   },
   "source": [
    "We can also try to visualize the relationship between some features, typically starting with the features that we found correlating. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "q_i96O8eEn9p",
    "outputId": "fc4f42f1-b11a-4175-f251-4d2ac03c1dca"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Front Camera Vs Primary Camera')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "sns.scatterplot(data=mobile_train, x='pc', y='fc')\n",
    "plt.title('Front Camera Vs Primary Camera')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-_Esn-NPFgJg",
    "outputId": "957a94aa-f3d9-48fe-a24e-5d90da89ace8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='px_height', ylabel='px_width'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tv07UzepciWe91WDNAb36AbBdd1b596yoxhNvtiecTPOOl9PjSpOxh29egrqKPFuZOY1kN17yeeCTc7jjsbeiPt/ZtBwN1YWu/14PpE9omVKN5n2i2QI7tfex5KSlqQFLyvIiyqT1wfY7l2K2Lwt9Ixd9m3qGIiPmxPJ2JRHGSZKkQ7Is19O+E5ptF2DmhO6kgMRLc8SjAWY9k5Ik6dYvVqdiGuxoEbRjIUHCd1vei3BIS6QrLqfBI9fxvPazM1cSJb4raw7o9Ssr3raZPteLMOBmP1hVSvDKmdPyaEfGtG2tyM9QUtBnpCbp7i/xmld2NYtuaibVc5bkiZhbnI0FJTmYXag/JokWBcqpvY8VqrF55+Go/AB6fXBV0cV6VBRk4rQ/gEOd/fjsfDDKnNRN4pto46SFINsuwMxipyXm+RmpON4zBG+KR4l8YeUKM5YEjmeRZD1TnJMYVzw08ExePQKgHov2vhEMjI5HlG9mI0m0BBZG0DtwVvnCC/KFiSlsWj0Hz7WeidD2x+raz+pcSfRF3Wg+2iU0rPJXzy+K0oipYUeG7SgleEmcE2SP1kazMkZr60M3Lcaj+9rQ4b+A5tVzdPeXeF2n2z2EunmINTNnWXIab3M/p/cA0idlTQ3Ye/wspkJQTMFoykGjPlDLLbnlsmMSZKW9iTBOLAiy7QLMLHZqYk4LFZVIoWtY4FkkE0UbaBZ6k9cMAbDT/kS4HjML1oGzPzCG4z368VkTXSaAxF7UjWTN7jxkla9HtCcnQ1GZ5MzIsB17Xl5nQj3nbh44NU+pDrgvfqCEVQvJ+gemeK21dg+hbh9iWXPWKH50Iqy1bu0BHo+E0fEpbNt7MuJzKzcharmVJOs3aGZCNM4kCJttF8DKonhFnhc+jde/2m7qO1+YE3EaBIBKXzq2fX0pRsenElqjyWNHFgs761jCrG2k1fYnio2wGejZA96+42DU50/dvQyzstMirssTWd4THUTW+gNjSEnyRKwfoZCMD7sH0T0YJheLSnORnGwuCqwZWQ6FZLx58lxEXHDAnAzr2fMuq/IpZDlV01ZtBBaa74fWufvBG5dEOHfzyp9T85TV1g2r5mD7vpNcSplYrrUz7dZNDaP40UBirLVu7gHtfSO4+xdvY21tmRIp7OX3u/DkXcssy+2GVXPw+H7z/UjG43jPUEKOgxGEzXaMoT6hazNpaRdGtRZCexoszfXi9voKhZzE83THEzqLaE5YWbYSWRtoBVYyQFppf6KHNKKBpV0bHZ+itkWGbDrpkQAbZD5qbxG237kU45OyYxE3eKNMqFMzE5iRYdZtYUmOF3s+7MGWPceiMmqq28Wqr1aL3OG/gKZnWi1t6k7NU1Zb1QmUWlo70dLUgNHxKWSkJmF8KqTEtdZrrx3Q9gCAnTYeiK2DnBWox78014uy3HTqGPYOxXetdXMPqMjPwMZVNbj/xQ8izJYq8jNMlaOW212HzkRlimXdrqjlKiM1GVv2HMNXry6bcXueEQTZdgnq8E/qE6n2+lNNzPtGxiJOg7SkCOrfuq1RUJc/OSXjfsaBgTx7uRElPQLgZMrYRA9pRAPrSvi0P8Bsi1lTgZmsUYsFaP155MxgVOY3t9N89w4FDU0fjMA6vE2FoNiI8sYM1tbNqU3dqXla5cuMMmf5wdpF+NkbJ5UyN69ZgEWlufj9sV7c/Yt3qGuuk/ODtb7PK86mztmFmxoT0hxDCzL+5Lbgs8ELUWNY6UuHJAH7jvditi8TIRk4OxzbNcfNPaBzYFQh2sBFs6W6inxTc0A9R8mBUBsCmHbbTQsj6vHY9y1JNAiy7TJ4FnNCzLUbSpKHbfekpwUE7GsUjGLpajcyN2Okmq13rAgYjQBsv3Op45vMpWTvrteWt075uYmPEwk0LnWiTlt7WJnf3NQYFed48fL7XVGari3rarllmHV4IzLDayOqHffSXOdIjFPz1OORcEWeNyKl/S/f7sDa2jLUluWgpjhbObjqOSHbVX7QNI7ad/30G3UMTfBYQuwHRiAklii28jNSI+S00peOv/rzObjryXeQn5GKb15bGRFb+pHbrsHC0mx0D7q7jri5Bzh14LRic0+T4W372rDhC3O4NeMzBYJsuwwzJ1KtsKanJFPtlvS0gE5pFFiT4Idfuxoneofxxomz6BseUzYtf2As7tc+sdau0xYXWQa+8ij7JsOp98xUcqjXFjNzpbM/oGQvBMJJFnj7+XK5hclITY7qzyQp9hqjKl8mNq9ZgC17juGeFdVI8gD1lQWms2vSDm/qtOVG7WKN+/Y7l2LDs4dtb+pOzlNfZlqU/87HZ0cizFuMUnfbIbtGyhZSZmbaRRkrzfXilrpyJHmAoeBE3PcDHhASe7xnCMGJELoHg3jmYIdy0FlWlY+mZw4hOBHCLXXlCtEGLvZp08rqqERlTq8jbu4BTmrNzZov9Q4FkZ+RilvqyiMyS49PhfCr1jOmQjQmOsx5xQiYBpnM6g1BbzEnwtpQXYglZbnM37IWWpZG4bQ/QH1fKCSjvW8EBz45h/a+EYRCYcNAVvkneofx8vtduGN5Jb715Nu447G3cMO2/fjsfBCVvvSI52N97cM6gLDaTsDqAx6ox6t6VhbODutvgFahfc+MXnQYbeGdK6GQjHc7z2PHG+3Yvu8kHt/fjvUNlcjPSOXqZ6tyMtMwPjWF5lU1Ef1ZkJGKH3x1UcRnZjTMVkCIwpN3LUNjjQ83XVOGFXMKTTtl0kBkhmjO9WSHNe6zfVnY3dyInU3Lsbu50RZZcmqe8swF9UGDgKy5RkTcCCxlyy115RHvKs5OwyO3XYNKXzrWN1TiiTfbsW3vSXzQNUitmwTJ9BrrJohsrp5frNS3ezCIn7weXle6z1/sR9btCWmKlXXEzN7j1h5glqM4idJcL755bVhuyFr+zWsrcf3CYvz49qtx0zVluGFxKa4qmtl7HiA0267DzonUihYwMD5p6yp+y7pafGVxqa6TztraMvz9yx9GLMSbdx3BjvX1SsSBeFz7WLkOc1rLORPtqxMFvHPltD+ghIQCLhKBppXVSj/rmYmw5KRD5WB2KcCXmYaW1s4Ic4Sf/+kUvv75iojPyvK8MWszK/iVVbMeRWZKstEfGFOcBmllsMa9bySoEJh4gNV2o7lgZFpgZx3SS1xEynrktmtQUZCJioJMlOV5I6IMPddKT+f93Zb3lBCfiXKT5PFIimJLm+3z7FAwoh/1HFcBc9p7o73HbVO3ycmQEpWoPD8dv21uRG+Mb06nQoi6Ldi6tw3XLyzB4rI8198fSwiyHQPY8Qxn/Za10FYWZHJdp572B3DaH8CJniHkZ6SiezCokOb8jFRcV+2jZpd65mAH1n2unLoQpyRJ2G0jaYBdWCG6Ttuax9O+Ot52yE68n2eusIjA3GlbVqNNjCUnhz89jwsToYQhAXZBzDdoMc3VSYTW1ZW5Wg8eUmHnwMu7vibiQVjd9vyMVNxaX465RdlYUBq+Ntdrlx4hp61DD9+8BB4p/E6jftVLXHTdVb6o9V0bZah7MIinD3TgqbuXYXR8Eoc/PR8hd4lmv03ry4r8DPyx7axyaNh1iH6AePpAh1KOGXly2+ZeD5OTIbz4fldUBJKbri5z5MaJF6yb4L6RYERmyksBgmzPULAWWkA/aYWRLV5wIoTWjn6U56cr5Xf4Azj86fkIez3aQlyc4zXc9PQ2VsCeY6cVoptIKZrtIN52yLF8P4sILCjJgccTvqbWO0DR5ITMgYHR8ZiQgFgcjLSyOCvLi1P+i5lMY3UQNDrQxsq52smDsFPjR9qen5FqKaEZ66BBxn7exkYc6xnCx73D+OHvTnBrlc0mLqLNyYHRcczKTkPvkOxI0hS3QevLVfOKMWdWFuoq8jE6PokqXyauX1iCvhF784nIj1s290b4sHuQGoGkpigLV1+Zb7t8XiTiAdgtCLI9g8FaaPXIHssWj2Qn86Z4MBWCshAScjI1fVcWkoGctCQ8dNPiiFPxI7ddA48UDmyvt/m46dhphei6MdndiHFrhHhHg4nl+1lEYHZhWKvdNzwW4ThJDpFEpomc+O5ehv0nz0GWEXGQdJsExPJgopXF2YWZXLdPTsLoQBurOPK864MRkXZy/EjbjcK88tSL1l5JAv7mV+9H9C9vuWbWUjdNWqy03Sl4PBKqCrNQVRgph0TramU+Efk50TNE7ZeSHC96h/TXMLsg5akRnAihZzCIq6+0XTw3ZmqkLSsQZPsShHqD1S5SrI1Nmo5S0LyqBi2tnVFXy+OTshIZxZsSDnH36sbGiBP+mq37DTcfs46dZsmaWaLLO9njbaJhhHgnvon1+1OTJTStrEZIBjxS+O9QSMbvj/UyNdbqzd3jkTArO42a5cxtrUo8D0bxOAgaHWhjqd0yaj8PkXZy/EjbjUIXWiX4RvPSqFxeWTFr0mKGUPG2PR5rtJX5pL7N0Ia3+/f1dejsv4DWjn4A4UyO6xsqqWuYHZTmptOJfm5sNcqXUqQtIwiyfQmDtkg9tr6eOsnmF2fjnhXVaGntxOY1Cwy9+Dc8exi7mxvRUF2I9r4RJXQW+Z61+Tjh2OkkeCZ7vE00eBDv6zje9zuxIZ72ByLkjbyrpamBemvTtLIa80tyojb3eGlV3DiYJPJh0Kif3R4HM33DQ6SdHD/SdpaWk8wfqwTfaF46eXAwMmmxSqh46jgT1mgCIj/qMINpyR6suMqH0/2jivO3Wvl1a305dQ2zikWlOVG30w/dtBiLSnMdKd8M4qEAiAcE2b6EQVuk7n/paFR2MhKYvzA7Fevqyri9+K1cA9tx7DQDMxus0WSPt4kGD+J9Hcfzfqc2RJa8sa5Gl16Zhz+fWxT1Do9HwvULitHS1IDuwSBKc9OxqDTH9c3Z6YNRohMNPbJF5ml+Rgpamq7FxNQUCjLTHDssmO0bnrXM6bjEaxaVYGFpNip9mRFESz1/9OpFEtzQ1jqjeRlLEx6rhIqnjrFeo+0cbtXy0z0YxPPvnsE3r63EH9v6orK7EhNP1hpmFcnJHtx0dRlqirLQMxhESa4Xi0pzuZwjE/lgn8gQZPsSBm2R6vBfQFmel2pnprVLI3DyGtiqY6cZOE0+4m2iwYN4X8fxvN+pDZElb6yr0UqdqDhas5NYkFSnD0Yz4TBII1useVpXUeBY/5vtG561zOnxI2tvRUEmrrkyjzp/WPUqyfHqrnVG8zLWN2JWiBpPHWO5RtvdX7Tyc2t9OFnOvY3V1DYkeYCCzFRH2wCECffVV+abstFO9IN9IkMktbmEwUp6UJCZZio4vlHQezuJe8j7yabgRHIJp5OW6CWPILCTGMcp0Po1kd5vN9EGAUveFpXmmJLDU+fik9yGyPqrGxvx5F31eOruZVhYmm25PKf6NdaIRXIhs33Ds5Y5uVapoTd/WPWaCtGjVqj70Eq5btyIEaJ2w7b9SjK0PR/2RK2V2rW0Ij/DVoIfp2FXbrXyc82VeUpZtDbML8lB887D1L6KNS6XhGBuQGi2L2E4pYEx0o44pVV1ynaLtcF+3DsMAI6HFBSnfT44pUXTkzdeOQyFZCVFsxqxvLE40Wsv+g5BvO31rSIW2kizfcMrQ7G2M2XV661Tflt9GMsbMTu219cvKNaN+qG3Rjtt9uCE3Krlp71vBN4UD3YdOhPlMPnA2oX42R9PosN/QfdGJlamHTPhljdRIcj2JQwnF1KjzSWRnBxYG+zRriF8t+U92yEFS3K8mAoBb53yozjHC4/kbkxUpxEvmzsnr9+JvBF7VTIWVT79RCAEnf0BeDxS3EiqEfEwM0bxtte3ilgcEqz0jZW1LJZzSp2x0AnH5Fit3XZsr3dPzwuzCX4AOK4IcVpu1TL6zMEONK2sxpxZWejsH8X2fSeVkKQsUpsIOQ4S/WCfCBBk+xKFdnFdVuVLSC2rG5uUXtISq0RYTe60C9vDNy9RsnASJOppP55aeKcdEu20pXdoDP/022NRmqSHb14SE5Jq5PBmpl1OHqpjSRpjcUiIheY2FnNKT+PL45i870QvjpwZREgGkiRgSXkuVs0rjume4ITttdlDg1GCKytww2Zfuy7mZSTj+9NBDAhYpNaqz4aVuT5TD/aJAEG2L0E4vfi7tQHbqafRoks22I97h3G0aygiaYkdIkxb2O574SiaVlZHZElL1NN+PJ3pnHZItNOWwPgkOvwXlNBbkhTWGBZlp8aEgOgRDyvtckI7GeuDWKxMGNzW3MZiTulpfI36sLM/gLbekYg8CZtW12DOrOhkLW6Ch6jpzQsr8umG2YPTcstaF7ffuVQJcapHaq200epcj7cj/kyGcJC8BOGkEwOvU4vT9dRzOOSpE9lg5xZn44k32yO0znaIMGthm1ucHRMnI7uIpzOd0841dtpCQk12Dwbxk9dPYvu+k3jizXaU5mZYqotZ6DmmxWuM4uH8FG+nXhrMOjvHYrz03mHsmDyGrXsjs1Nu3duG3qExx+rHAx7HUr15YUU+3XKcdFJuWe2a7cvicsK10kY7cz0R5+xMgNBsX4Jw8jRvR2tjpBFn1bM/MIbjPWznMTN1cvrai6V5WVCSE/M02FYQT5s7p7VMdtoyu5Cd8j0W0NMQmW2XUzdPVsfnUoq7a0XjF4s5ZaTx1et/VsKw0fFJx+rHCx7fH9a8sCKf8TR74J0XrHb1jQQVQqsHK20025eX0hyPFwTZvgTh5OJvZwM22rRY9UxJ8uiSaTN1cvrai7WwzS7MVDaSREY8Nh+yUF+YmMKm1XPwXOsZ5abBKccis21JhOtQFvEw0y4nTT+srBt6tsSdA6MzbnO2olyIxZxivaMiP8Nw/FkJwyoKYp+hkweseWFFPsk8X7ipEb1DYwiMT6LSoXbrwcy8tLtfW1nLzLxTRNtyBpIsxzduo1uor6+XW1tb412NuMDJydHeN4Ibtu2PmpS7DTTbPL9j1TM/IwV3PPZWVJk7m5Yr6eGt1MkpkM0l0bXYLMSy/rQx3rS6Bk8f6MDA6LjtRTsRxsINrQ9vu5ycC3rrBgBqG1nv37G+Hk3PtM64zfnAJ+d01x4WYiGHtHec9gcsr7NOzDt/YAyfnQ9GZSR2Y6yttiNWZFG9DmSkJuH2HQe55mU8yKyZd8Z7v51JkCTpkCzL9bTvhGb7EoSTWjurWhse7TOrnqf9Ad1Td7w9ohMpzKEVxLL+NE3h1r1teOruZZiVbT8td7zHwq2NkrddTprmWAmfxnp/a0e/JdOzeMOqljEWckh7h5111okoQPesqMYTb7bHZKyttiMWDqzadaB59Zy43b7ywMw7RWxtZyDI9iUKpxZ/qwsB76ZFq6cRmU4EE4BYYibby7EWahnyJbFQxztVutP2wmbDp7HePxU55HHdnC/lmOV21lmrUMu8JCGmRMxKO2JBFrXrQEiGqXkZD6UB7ztFbG1nIMj2JQY3iJnHIymbTe9Q2NbWqFy37WnjrdF0EnpjNtPt5S71hTreWp9YkEO9Ni6r8kW9f8u6Wjzy2omI52M15tq5VJGfwQw3CdBNY8wc5N08CPOUXZGfgR3r69Ha0Y+QDLz8fhc2r1ng6uFAKw+JPr9jsQZp+4SWDTKRD216mGkH0ESFsNm+hOAWMaOVu2VdLa7I88KXyTYFSAR72kSH0ZjFy15OvdEXZXuR5AG6B80TCjdlMhG0/W6Nj5n2qecZyW56dti5fjFqo3ae6xFcp2zZeQ+mattxdd1f3diIE73D2LLnGNbWliHJA3y+sgDXVvuQnMwXEdfNgzBP2ax1+SuLS7nbYAVqeSjN9WJ9Q2UUqUwkZUAsFBa0OVLpS8e2ry/FhYmpGb//ib2cD3o224JsX0Jwa+NnlUvs9RJxcU0EIsYDozGjOWyV5nrxr7dfg5Asu9I+p50ajRbqyckQPuwejMgqqUcW4q3tV8tXaa4XH3Wzw1RaLT+RHMGslOvG5mzlYNq8ek5EsimCJ++qx9+9/CFur6+IIIpb1tXiq7VXGNY1FJJxtOs89h4/i5Ac1mR2DwYdO2gd7Tpv6GAXz4O4ehwqfel48MYlSEmSEna9NZJHu3tGvNckgcRA3BwkJUn6OYC1AM7Ksrx4+rNrAPwMgBfAJID/vyzLb0uSJAHYCuAGAKMA7pJl+d3p33wLwP3TxT4ky/JTbtZ7poEsFB/3Drtypc26Rib2enZtVJ0kxzNt0TMyQ9BegZbmevHNayvxrSffttU+vT5nOTXes6IaP3n9pOnx1jP5mZwM4cX3u3D/ix8o7XnopsW46eoyJuGOdxZMrXxtv3MpXt3YiL4RZ4il1fY53S9qGZlXnI09mxrRM8TXRjfMvIzaR5tLLNvZjNRkrK0tU4g2KW/zriNYUpZrOqxp86oaJUutnfWWlH28Z0h3XQiFZPQNj+HexmoAF8l+LEyYZpLPjHadW1bli6qnE3vGTOoTgfjA7QySvwCwRvPZPwP4e1mWrwHwv6b/BoAvA6iZ/q8JwL8BgCRJBQB+AGA5gGUAfiBJUr7L9Z4xUGdT/OCzIXhTnM+WxcpQRS5FyAJvBU5mqCQaoVhnwbMDo+xf2oxqt9aXR2WDM9s+oz7XO1yRfzuVHe/D7kGFaJOy73/xA3zYPcj8Dat+H/cOc2X8swMa4dvw7GFIEhzLqGY1I6GTmQy1MvKVR/fjo+5hLKvyxS1rnFH7aHPp5fe7sGVdrfI5IVLFOWlI15BwbXks0GRg27423FJXbnu9JWWTQ4IapGwyNt968m1s33cSj+9vx/qGSpTmemNmL00OU3Zl3my2TrNl8+wtTmVOtdonbvZBLMoX4IOrZFuW5TcA9Gs/BpAz/e9cAJ9N//tGAE/LYRwEkCdJUimA/wfAa7Is98uyPADgNUQT+MsW6oWCOGWQRbrSl44d6+vROxS0NcloKXSbV9Xg+XfPKH+X5HgtTWjehc5owSAL6/62c9QNlDh2Jhr00hMD0SmOr7kyzzahMupzo8OVdkO3s5gTbZy2PT2D7Paw6ne0a8jWYY0HsUjNzWrfrCz9OeZkaup4pG5ngcgXSYpUmnuxPXoHU2+KB5vXLMBXFpdGpb0uz8vAoityLPUXSwaSPDDtOKadO/7AGHUtV68LLLJ/a325Lce1WJMyJxUtNPDKsBtzmrcv3e4Dt8sX4Ec8opF8F8DvJEn6EcJk/7rpz8sAfKp67sz0Z6zPoyBJUhPCWnFUVFQ4WulEhXqh6B4M4pmDHbhnRTXqK3MxFJxyJLGE+oqsdyiIiSkZD7x0VLFR3H7nUpzsG8GRM4MIyUCSBCwpz8WqecWG7+KJ5sBzzUcW1h9+7WrGtXGSqTbHCmYjr7T3jdj2rDfqc5r3ObHZ1h4G7F7BluamU9tTkstuD61+5BrfKZMSlplNLCIbsLz/T/lHsOHZw8x+djJqQLyjrBDw+A/whATVmrSc9gfwdy9/GBUxYsu6WsP+YsnA6vlFWFKWp+ssrpYnIDp++ZZ1taj0paPDf0FZy5M8iCibNTZLr8zDn88tsqRlDoVk7DvRa2kNtwq3zcF4ZdjpOW1mTXS7D+IdmlTgIuJBtv8rgL+WZXmXJEm3AXgCwBedKFiW5R0AdgBhB0knykx0aBeK7sEgnnizHdcvbMB//c+Djk0y9YYVCsl48q5lyoYmIbxp7HijPWJDnDMrC1WF9mN48iwYZGHtOj8atYE2r6rBhDbwb5xB23x5xsUJQmXU51rSMisrHI1kaUVe1GHA7mK+qDQHD920OMpme1FpLvM36vp93DuMo11Dir0sqYMTdrO0zTIWYbBopNEjAWu27tftZyftRhMlZKPZpEhmkgGpCa0kAbIMlOV5DZ1QPRLw8M1LcN8LRyNkQI9o0+RpXnF2VNs27zqiRFAha7m2bNbYVNqwEe7sD+CzgQsRn302cAGd/QHDNdwq3D7Q8cqw03PazJrI6oPeIft9EE+7foFoxINsfwvApul//wrA49P/7gJwpeq58unPugD8hebzP7pawxkE2kKxZV2tqwuZdkN7q90fZUe8dW8b6iryDRfqivwMKtmqyM9QnuFpC1lYR8am8PL7XREbaEtrJ9YsLtGtRywjmNjRBjtBqHg2F2qyIcpYWpUzdX9/viofLd9uQM9QECW5XiwqzTUMXUbqBwDfbXnPUVJotFna6X9eOdP2/4FP6OZR2n62EhOfhkSJretWUiSyXnQPBvGT18PRSrwpHqyro16aAoict/kZqWhaWY25xdlYUJKD2YXsPmbJ00+/UUdtW0qShN068uX02BBSlpGWDCkwjl2HzmBgdBybVtfAPzKOKnaWeltw+0DH209OOzeaWRMzUpPpN7EpSWjvG+Hej3jiy5Pbv4HR8YSKg365IB5k+zMAf44wYV4FoG36898A2CBJ0k6EnSEHZVnuliTpdwAeVjlFXg/gb2Nb5cQFy8Tjq1eXxUwzFRifpC4uo+OThvGaOwdG8ei+tghy/Oi+MFHXEmm9tpCFdcueY1HhvIw2IjuhzayQc7vaYLuRHuKtAXUyYowbpNBos7Ta/2barZUv4vxm1M9O9a0VGXHjwOoWIbMiN+p52z0YxLa9J5VQe3rtZMlTZhqdaBXneHXli3dseMZDL7LK1r1teOJb1ChmjsDtA50ZGXYyeo4ZmR2fmqLexPaOjOH2xw5yzWFWfHmaXX/TymrML8kRCWniALdD//0SYa10oSRJZxCOKvJtAFslSUoGEMS0jTWA3QiH/TuJcOi/uwFAluV+SZIeBPDO9HP/W5ZlrdPlZQ21lo/EXY1lBqvKgkxU+tKxtrZMiVjx8vtdqPJlGtpbzspORYf/gqJdIlBrAXg1sWsWlWB+STb6A2NoaWrA6PgU16ZvlvzaJTSJYA/r1OZil7QA9kyc3Ai55RbB4203S76237k0ymZb289O9y2vjLgVctMtQmZFbqzOW5Y8FWen2cqyqzc2vOPBcrYkYT7HJ90zv4tFuDyn1jkzB0kzMuvLTENLa2fUTeyN15Rxz2HaGLZ29FNl1Y5dv4A9uEq2ZVm+g/HV5yjPygC+wyjn5wB+7mDVLkmwnCVry3JQU5ztmmlEZUEGNq6qiTIFmQrJhvGaW5oamMTGbJxfKwurFbs2u4RGj8wlckIeVt1iRVpY0Bt3K/3pFsHjbTdLvl7d2KhrXmDmHU7DCZLvlHzxwux6YfUQxpKnioJMVBRkOqKh1oJ3PPTCfHpTPKgocFcD6kY8dqdh9iBpRmarfJnYvGZBRNkPrF2I7fsilU/EjhtAlBz0DgWRn5GKW+rKFWVXWrLHcbt+AXuIhxmJgEtgOUu6nVGsc2CUGiuZZZOojtc8Oj6F7XcujfKCr8jPcD05jd4Vqp5dmxGhMdoc1ZtvfkYqbq0vx9yibMjTEQH0Ik7EC0YbTixIi9N1ZsEtgsfbbpZ89Y0ElRi+dt/hNOySfCflyy1YPYQZyZMTGmot7Ebi8EjhMIazCxPb3ID3IBJrsz9emfV4JCwszUbTymqE5LBmezg4gYHR8YjnvCkeTEzJys21Wg5IojPiN+VN8eC+L8/HP6+rxfd3HXH9ZluAD4JsX0KoyM/AjvX1aO3oR0gOm3JsXrPA9Qlm1iZRHa+ZpLtWRzJ55LZr0NE/6nrIItYVqpFdm5Fm2mhzJJvvwk2NeLfzfERUg02ra5Cfkapo1xMlTJOT5gmxcr477Q9gy55jyhUtAGzZcwzzS7KjzDZoG7E6+o4Ttw287TZLmLV+ETzmJlbhVkjEmRCizM4hzOqBwWq/2InE8fDNS1BXkYeKgsTWgvIeRBLB7E9vDSH2/wSluV5qSMoHXjpKlQNZRlSAgod/exy/bTa+CROIHQTZvkQQCslR3sdb1tXi+gXuxUklMGOTqI3XPBWCKU99J6/Drcar1SNNvJujxyMhJEMh2uRZtZmNG222CifNE5zSHBuRYH9gLMpZtnlVDT47P6o8a7QRO2mLzNtuM4cRVv3MpFbnhZshERPBj4EHsbYBttov8YrE4bQZnF55vGutm2Z/vG3QW0NoN9ItrZ1oaWrAhYkpFGV74Q+MocMfGZqRyIEsgyojvcPGN2ECsYMg25cIaAvK5l1HsKQs1/XJxmuTSIvX/NYpvymtuJPX4Vbj1eptUGY2R6O06KQ+iRCmyWnzBLukhYcEpyZ5FKINXLy5+NHXrsaeD3uwZlGJ4UbstMaVp91mCBCrfrubG9FQ7WzMNjdDIrpt/pJIvhBmDnBW+yUekTicdpI1Ko93rbV7kLN7kDSaN7TyN69ZEBW3XU8O4mE6JmAOrqZrvxwR65S3BLFII80CWdi1KZE9HklZyBuqC3FVUTjJDTltq0/1aqi14uQ7N0wNyCJn5R3qdpG2AOZSZrOeJetrItnZ2ekrN8CTinl0fIo6J9rPBZRnjeZNvOYVS7600EuK4TSM+oK3zjRYka9ESYnNA3Vdj3YNYsueY7qyS+DGGuVGm9r7RtDZT5+TR7vOW+prvTkeCslKjGo1aGutmTWZBr39jQe0eZOfkYq+4TEc+OQcTvsD+OK8IrQ0NeBnf1mHlqZro26k9eQg0dZmATqEZttBuBX+igfxzvjm8VhLpmHXU99une1eodKSCfBqQVhtX1iajeuu8iWUnZ2T181OaBnJBlaa643wwu8PjBnGZx+bDCkk0WjesL4vyfHqJp3gjXFstx+YSTFSkxx/l5trjFn50q61lb50PHjjEqQkSZbNDdyCniO2UdZTN9YoJ9YUWpsevnmJ4m9CEJwIYe/xs+g6HzS9D+odJI/3DGPLnmNc4W2d8BGxo/3Xzhvi0PitJ99W6vPQTYvx6L42dPgv6Pr5sOTA7RCKAvYhyfKlmdW8vr5ebm1tjek72/tGFG9hAm+Kx/VoIIBzRN/qwmzn/eSdM22hYLX5+gXF6BwY5SYNM7HtVuGUnLb3jeDuX7wdZZO9ZV0tvlp7BdPeWh1tZndzIzUWvJHN9vY7l2J8Ujb1G/I9AGV+TU7JuP+lo8wNlgfvnPbjnVMDUXbpy2bno77Kx+zzLetqcUWeF77M6JTnTo2dei0pzfViKgTlgGNXztVrbWmuF+sbKqNIF6nXgU/O4Y7H3ooqY2fTcsdNbYzqSuBN8UT4Zri1T7ilAGK1qWlldYSzH2mnlahYrHe0NDXg9h0HIw7bSR5g9fyiKNMLgnius9oxaF49RwkIoG5XLORBwF1IknRIlmVqJiih2XYQ8XTycUoDYnVhtqM9SpTQXmYQCsk42nWeaS/L256Z2HY7cErLWOXLxIM3LkHTM61MPwUyJ+ZtbMSxniF83DusEG2i2TKaN7TvZRn4yqP7mW1gtXHhpkZ81D3M1HBa6QdWUow1i0t0+3zzriMKCeKd42bWGG1qc21oMruET73W3lJXHmWbr+7LeN/6sfaFJM/FurgVMSYjNZlqsmJXq89q09zibKWv1fKt3Qd5lDosjfT4VEh5d/dgUCGo113lY8pTPNdZ7bxhmbepfXVixRsEYgdBth1EvBd1u+HK7BChmRJNwIkrVUIkjvcMcbU5Hs5ZieQQpoZTcuLxSEhJkqKSOew6dAZnh4NKZBjS/jULS7CwNIdqnmO0EWu/P/DJOd02sK+/x6ihJolGy0o/0JJiaIkbLenFrkNnIEnmyRcvaVGvJd+8thIXJqYiEkfZJXzqtZa0Qw11X1oxI3By/rD2hdXzixw3F7NjsuJEmxaU5KClqQF7j5/FVAjKe9X7IK9Sh3W4O+0PzDiHQPW8ae8bodZfbWSQ6O0RMA9Bth2E0aIeKwLkdCKEDn/AsK7xPmjwwM41uHq8CJG4t7HasM3xsON3wqTHLRl1Uk5oyRw2ra5BcbaX2X4nDkFW7bwD45O6Gi0r/cCjbWb1U/WsLJTmetE9GHT8UKy2qc9JT4l4NyF/Vt8ZCsmQZeBHX7sabWeHkZ6axBwPMsb5GSloaboWE1NTKDAwnXF6zrL2BZbJgx0YpV8HnFmXWW2aXZiJUEhG29mRqIzCFfkZzDqyDl+0w12s4vS7BVr9ic02IBwcL1UIsu0g9Da+WJIupxMhHP70PC5MhHTrOhMWQDP9ojdehEjsOnTG0EFH+878jFQc7xmCN8WjeJInyvjHQkadlJOpUHQyh6172/BnV/kM22+nrUZtYH1fWZDJ1GjZ6QcjbTOrnzatrsH6hkq0tHY6figma8ktdeV48JWPoshf08pqriQ92kMQyzHvX2+/Bt9t0ThZM7LQ1lUU6B6u+4ajbyDshnuMlQNbrExW9Np02h/Ao9MEn5g2PbqvDXUV+bo3P7yHr1j2pxug1b8iPwN1Ffkzsj0CfBBk22GwNr5YesQ7mQhB7VBmlJ420RdAM/2iN16ESHQPBvHMwQ7cs6Ka6aCjfqeRI5cZ6BESq+Nvh6TzaoidlJOzw/R2fjZo3H67PgZGbZhXnI2ffqMOmWnJKM5OQ0VBmNxo59eWdbUoy/NiXV2Za/OF1U+B8Sk8vr8dO9bXO34oJmsJy9RqbnG2qSQ9ZI7Qxu2+F47i1Y3R2fKsHq7vbax23DzMarQms4ilyQprr+sdCqLDf0HRpAPhta9veEyxI6/0pUckaWFp21l9bMUG202HXbOg1f9y8t25HCHIdowQS5tmu4kQfHcvw/6T5yDLiLD1M6orWUDIRvfWKX9C2Qub6Re98VpW5VNIU/dgUHEyo10Lq99p5MjFC7MZyfTaydtmVv2saIidclZitbM017j9TmjXaG0IhWTsO9GLI2cGEZKBJAlYUp6rpL6Ox4GU1U8k81xKkuR4HUhby/LSqZEXFpTkUN9pRJBZ49Z2dhhzi7OxrMpn6dCpfa8d8zBSnpogArB9a2THqdCOyYpZcyuroe60hy8nb9rcdtgVEDCCSGoTI9gNrG8EdYIBWQa237lUeZ/6WtUoCYTHI2FWdhoe39+On7x+UiHavHVNhAQSLJgJ/q83XoRI8CQ5UL9Tz5HLDIwSulhNcmBFRnmSy7gFVjsXFOdgx/p6NK+egw2r5qDSlx7VfrfmY2d/AG29I9jxRju27zuJf3+jHW294YQfgPuJRmig9VPzqho8/+4ZeFM8KM5xx6/C45GwpCyXOkazC+myaJQ4hzVuR7uGotYaM2Osfi8xD9ObPyy5P3UuQF3/WAlfeOcJ77pqZm1y8r1qaOXt1vryKDOm+1/8ANu+vlS3jk6uLeqybqmLrk+s1iyByxdCsx0juGnTzNIA7NnUiJ6hizZhvz/Wy6UlYCaamSbrehoOp81lnHTYM6NZrMjPwI719Wjt6EdIBl5+vwub1yxQxotXO6t+Z9/IGB7fH63lM0vwjDR2VjWoVmQ00cJdVuRn4I9tZyM0y3/75QX44nx6Rjan52Pv0BjVPrquIh9VhbG9IlbPnYWl2Xhlwwoc7x2mhkB08/2zslPR0tSA0fEpwzlsdCtDG7e/XTMfw2OTuLexGid6hrCwNBtVheaikKjfS8zDmlZWY+mVeaik+Faw5J5Fqp+6e5mteWLXqdAqrKznvKHuLkxM6cY5d3JtUZdlFL1GQMANCLIdI7h5hcxaEHc3NyqLWXvfCPWZMsomyCIxPGTdyQXSDYc9no0oFJKj2rplXW1UCl2z73SK4LEISXpKEkIhOcKm0YxJjxUZjXcUGu14nj43omiWSR9vWl2DM+dHI8iuW/ORFXFkdHzSVrlmwZw7OiEQY/H+5bPZsZAB40MQGbd5GxvxUfcQzgyMIjgZwvbp0IneFA8qfZmmzXa07x0YHcf8khz8+dwi6vMsuc9ITWaOv5154sS6akVxofU5IeEj+0bGDNcSo1B3Rm13cm3RlhXPNUvg8oQwI4kh3LpCNrp61Xtm7/Gz1OtBbV07B0a5rvScvJ6Pl4kCKwlI58CorXKduuJlmQU07zwcMYZWroDNyqhVkxW3wNIs9w6NRT3rxnwkEUfU8KZ4FAfJWIE1dzoHRmNixmJ17vLMEY9HgiQBJ88O48LEFH78h48j3nPfC0eV9/COsdm5yZL74pw05vjbmSd211Wr5n3kvcS5+4k3w+ZR3/r529zmgVbXCCfWFmJe2TsUxGPr61HpS8euQ2ewabW+mZCAgNMQmu1LADwaANYzU9N/Gl0P8mpWnLyej5eJgpvvdeKKV3E+oySPUI9hLCLgJFoUmnhrlmcXsuMP24FZrWS8k0zZeT/PHOkdCuK51jP43pfmOtZOM3OTJfdAdMQZMv6zCzMtzxO766rVtUAdVcaqczfvGkGTcTtrC+125YG1CzEcnIAHwOPfqkeSJCWUE7/ApQtBtmcAjDZanoVYL6wfgd4mxXul5yT5cuIa0crVabxNI3jg8UgYHZ/Ctr0nIz5Xj2GsCJeTNqIs8I4jK5Z1rDTLbhw+rJhTuSXDvOPg9hwqzvFiYHQcZ4eDcZurLLnXG3878yQ1WULTymqEZMAjhf/mHQ8za4G2zOsXFEf1sd7vaTBaI/Rk3Gqf0Q4YD77ykZLgx5viwW4XQu8KCNAgyHYcwbNQshahL84rwrHeIXQPBlGam44vziuKijOrvXpVbwLpKUlo3nlYiTYC6G9SZjQrTpEvu9ocqzbfMyFBD2A9k2EiHRp4YGYc3dIsm4HThw8rWkk3ZNjMOLg9h0j5W/YcM0wsFWu4cfg87Q9gw7OHI+ZypS8dgS9NYfOuI4bjwbsWsMZ4XnG2q2uJG7dwrAMGydganAihd0g4RQrEBpIsxz8kmxuor6+XW1tb410NJng3rva+EdywbX/UIvdv36jDf/3Pd5XfPnTTYtx0dRmSk/nM8LVxR2+tL8fcomwsKM3B7EK6doQcDmJpLsDzTtahhdV3PNqMeLTVLIxkKB6p4p2Adjw9ErBmK/84zoSxM4MDn5zDHY+9FfX5zqblutEc9PrByo2P2fnk9jiQ8vsDY0hJ8nBFO7H7Lm3sbKciJRmBJgPf+cIcPPFmdHQj2njwrvesMd6zqREfdQ+7tpZYlXE9sNqi1my3NDXg6ivzdctxMiKWwKUNSZIOybJcT/tOaLZjDLMpgVmn88Ofno/47f0vfoCaoizmwsGyh1u4qRHvdp7HfS8cNVxE7WpsrCxadq4f3bYbjTeMTBYSzZ6aB6x03PkZqRG3MHrj6PTYxXuztZOkipV4x8ohzOx8cnsO2SnfzJhq+6vSl45/uHkJzgcm0HZ2GM+1nlHCKDp9kCX1vDAxhU2r5+C51jPKPEjy8IewI2uB0XrPGuOeoaCra4kbt3C025VNq2vw9IEOxYxyYiqkW8ZMVVgIJB4E2Y4hzKYEDoVkZKQm6zo2qn/bMxjE1Vfqv1e7YIRkKAsvKceNNPJuLVo8adVnuhmFHowIx0w4NKjBSsfdtLI6wj49VuPIc3vgNhF32iTD6pV9IswnJ/rb7Fqk7q/SXC9ur6/AvU+1Rvm+OLluhkIyOvsDUcSYkMWB0XF8vrLA1Hh4PJLheq83xm6uJW6YHamVDR3+AI73DAMA1n2uHLIMtLR2Ys3iEt0ytHMlPyMVx3uG4E3xoMqXiYr8DHQOjAqtt4AhBNl2GHqbAU9K4FlZXqWcPR/2UG0S//HmWvzr3hMR7/WmeFCS46UmndHbXGPlROdWZAzetOrxtOeMt2Z0JoE1ngtKclDpS9dN72wVZuasWm6rfJkx0Xo5fUNhdc7bIUTxIMksmF2L1P11S115VFSObfvaFNMEJ9ZN0s7jPUMRqe6DE+Ewlk/dvQyzstNQkZ/heBKqKl8mtt+5NCIp1JLyXNfXTLdu4cgBocqXiQvT42xGdrVxxtc3VEbsxbS080LrLUCDINsOwmgz0KYE/usvzlVixBKtxSn/CGYXZkZsCM8c7MA9K6qR5AFWzy/CguIcTMlh0xH1pD8fHMftjx00ZU7B0mRICNs8O0UM3SL1RpqYWJtRaEmFmcydlxtoBIw1nsd6hvC9L81DWZ4XBZlpjo0ja84uLM1G92AQFybo2e9IDHu3QysSOKlVpPVxpS8d6SlJOPDJOSYZtjqf4kWSWTC7Fqn7i5V9UJKc0/KTdrJuP2XISj3dSEI1PilHJIV65LZrbLeJB25qzvVCNeplRVb3F+2gdf+LHygHLTfnv8DMh0hq4yCMEjmoExN0DwYRkmU0razGhlVzcM+Kajx9oAMbnj2sEBBSTvdgED95/SS27T2JCxNTSE1Nwk1Xl6GlqQH//pd1aGlqwOer8vH/PvMu9d16CRFoiQM2ra7Bd1ve4058wAOepAwkAcGBT86hvW/EkYQJbiQuYYGWOOLVD7qxZc+xqHE5dc7dxDzaepnt11jUiZZkg2jr1OPZvKoGv2o9g827jqAgM83RcWTN2ecPd+GOx97C+5+eZ8otTzKpRIR2zlT60rFxVQ1u33FQN+GJ+nBk5uDqVHIqf2AM96wIr5cbVs1Baa6X2t9G8m42QQxtjdH+1iPBsdsWtVwZ1dPs+ma0XsY6kVgs1yZtXwEwTPSj7i+9g5b670Sf/wLxgdBsOwieKzr1td+Fieg4yQB0Nc5q7dOSsjxcfWV4ph/45JwlcwqtXdvhT8/j6QMdihOOUyd1oytoq9qveDkB0rSyrMyTRPNBEJwIobM/gKuK3HXsIs8nooMPa1Mnmft8dy/D/pPnIMsXE/YA4IoLbGb8WXOW7LfPtYazzZGslFq5jbcNsx5Y/UILA3r7joO6GmM7cuRUqvHPzgeV6BvkENbS2hl1YDeqJ485DC3W9O7mRvQHxlBTlBURbu/hm5egriJPSRFvF2Tt33XojONhDY3WS7NjZWfuxXNtCoVkHO06b3hTonYsPTs8Bo8EhOTwzXT3YDjGuzqgWyLNf4HEgiDbDsLoii56k0uOsMlTP0/bEB66aTGadx6m2ofZMacgJ/7eoaBukhQ7MKqDnSviWDsBsjaJ/IwU6kaVpLk/8qZ4kJFqfupZ2ZzM9mus7MuNNvVZ2Wl4fD99bmjra2fDZs0bsoF2Dwbx9IEOPHX3MsiQI+Q2keOxG/WLes7oHdSJjNiZn044Vp72BxSCS96/bV8bdqyvj+hvnnoarUVGCVbqQjKWlOW6drhXy9UzBzvQtLIac4uzsaCEHZbVDPTWSzNjZXfuWZUpu2uU2iae92ChDXtIDnobV9Xg0X1tAETadwF9CDMSB2F0RQdEXmUtKctlPk82hN3NjdjZtBwtTQ2KIwYQfb3nhDmF2etVs1DXgWiCyfVhLK/k7V5d0jaJLXuOISXJg+bVF6+4gXD/LSjJiTLTKc5JM11vK1e8rH7tUPU9aT/LtMONq10jWeOZS4D9a2+WGdXz755RnhkYHces7LSouaOdo0QrryZt8TLfMdMvPPPezvzkHUs9sN6fkiRFrGW89dRbD436zg3TNLWsnPYHFE36j2+/GjddU4YbFpfiqiJ3zeAAoCI/Aw/dtDhirB66aTEq8jOinrU796zIlBNrFKl3SDY201E/rz3obfv6Utx0dRmevGsZdf4LCKghNNsOwqxJA6/GmWifCNEmUJ/CnTCniJWmbnIyhD+1+9Ha0Y+QDLz8fhceunFJTK7knbi61G4SJBzYXz7xllLmX39xLiTIuCIvA3kZyfjeF2swNDYFjwTUFGdZSh9u5Tqepak6/Ol5bNt7MqL9bkWMAeiOo3qyxivPdk0UtO+ZleXFKf8IBkbHlb7SmwNOx7LW6zMnzGNo/cKa9xX5GcpBOCM1WYkGQ8A7P+2uTXohUItzIt9vRYuu7edYRWhSv19Pkx5LdA6M4tHp6CqSBMgy8Oi+NtRV5EfVxW4/WRkrO2sUGeePe4dxb2M13jhxlstMh9XOCxNTSE72zKjQqgLxgyDbDkNv82XZT/JMVp6Fya45RSzsn0MhGa9+0B1h89i8qgZb957AlnW1UamHnSb6ThBK7VjQvNR//IePw86vvzwMb4oHW9bVos5mJA0rm5NeYgdS1y17jqEoOw09Q0Hc21it2COS7+2SDBaZINo7lqzxyLMTJgra98wuzNStFw9YcuabDttGymStC26Zx7D6ZV5xNn76jTpkpiWjODsN5XnRUXRoYc5456fVtUkvBCrt/WYVBrR+fmx9fUxt8VmyUtbU4GpWTBp6h4Lo8F+I8DEB6L4SdueeFeWOVYJPG+fmVTXY80F3RKSvJWV5Uf2cCPHlBWY+RLr2GMApLVciOrqZhV4K3S/MK0RBZpqrjo5OpAXWjkXz6jlUR9f/8eV5GA5OQZLC8WpvXlqGqkLnSauRDKjTZkuQ8N2W9xQyTYsdS5J0EAcgnvT2emCNeUtTA3Vzs5PdL1HmxTun/fjjiXNKpAJygNmwag4e39+uHDZYYSFP+wOmUqNrwdsveqEP12ylj9mFiamYOSKrZac014tb6sp1iRFpE2+aeJpsVvrS8b0vzYs6+FuVKSN5Zq1JZF2JpUyz5qpRCni7tze8a76Z+vH87p4V1XjizXbdeifqGiOQeBDp2uMMJ7Sp8Yq6YRWsDYalmUjyQAnr5uaVnFOaUCNH10pfOrK9KfjXP1wksZW+TFsRC6zKgFqr2N530UQC0E/SQTYhu7cLrDHfe/wsus4Ho+yceUkika8FJdl4rqkBnw0GUZqbjkWlOXGdF3qRM2T54vxvaWpwLdkUr6yw1qan7l7GvDrnPZQ6AVoIVAC47iqf7s0A7zpC6+cO/wWU5Xlt324AfPLMWpNIlmAnzbmMYEbb7MSeZPbGw6qpI2s+1ZblYPd0giqr5p4CAjwQZDsGcMoGMNZRN6xCb4NhbSz1lQUx8eJ2yi5dPRahkBxV5v9YswB/rSEx971wFNdcmWdr/OzKQEV+RoS5TpIHVNlcWJptuAnxQo9MfO+59zBvY6MSBpHnYKqWr/yMVHzz2sqo0Hzx1DqxImc8cts1ePCVj5TPugfNJ5uyYx5DA2ttGh2fTIirc71+cELjyCrfqYM/jzzT1iRyu0Tgps24Glb8jmK5J9HqV5GfYXgTxhrnmuJs7sPrTNh7BRIXIhpJDOBWlI9ETFYC6Hup0yITbFlXi+uqfTEhR0YRJMyCaNZmZaeipakBv/z2cry6sREhWWYSqXghFJLx+2O9eOS1E7hnRTWaV89BQ7WPKpvl+enckRaM5JAQfPWYN68KR/wIToRwrGdI+Q1PhAK1fN1SV64QbfKsnSQcTswpVhtOnh1RzHe8KR6U5qYz1wUnInjwgLU2VRTE5v1G0OsHJxKwuN3PPPLs8Ui4fkExWpoa8LO/rMN/3LMcLa2diqyQesX6oJOoFqbaqFa/P9ZrGJ0kVvNJQIAFodmOAdyI8pHIdmRGmvx4X8k5paVgjcHy2T4AiZfwRE1OyHX8S+914R9uXoL/+cLRCEe4RaW5XGUayaGa4P/wa1ej7ewwpkKIsAn/uHcYC0tzUD0ri0ujq5YvVlY3K1pAnjnFY0/OasNk6GJWwEduuwaLSnOi1oUt62pRkZ8Rs6tr1to0uzATswszLb3fyVjtev3gxI2hnX62IwvaRDxq232S0fP+Fz9wbL/gRbyj6JgFr4mmMAURiDeEg2SMYNYRxKicvuExfOvJty07UNHKdGJxJJm59h4/G5Vpy66jHe/7Y7XQ6znrVPkyE+4wxHLE+tVfNSA1yYOewSBKcr1YVJqL5GS+Sy8jhyWtgxvLGfPHt1+NhupCrs1eXSZxOHRiHnxydgRfeZTdFiecDns0qc7VYTCnQsArR7qwec0CV+REL/IJa21K5IylVp3lnIBdWWDJM0GlLx3bvr40ps6orLq44ZjrFJxweBcQcApxc5CUJOnnANYCOCvL8mLV5xsBfAfAFIBXZVn+/vTnfwvgnunPm2VZ/t3052sAbAWQBOBxWZb/yc16uwEntKnqhezexmpHNHpOLo6s8EotrZ3YvGYBPBKUVPNubB60929ZV4sr8rzw2Qi5x0Kia/C1YGnZfNP2qVdfab5Moz7QOrg9c7AD96yoRkVBOjr7L+CZgx0YGB1nZlml9ZtaG7vrkH46dV6EQjKOdetnlHNCi6aNRtM5MIqmZ1oj3qsu06nDo9E8dypWuFux2mn9EM8Mnk5qVFlOmrF2RmXVxcy+4masfhpEWD6BmQK3zUh+AWA7gKfJB5IkfQHAjQCulmV5TJKkounPFwL4OoBFAK4A8AdJkuZO/+wnAL4E4AyAdyRJ+o0syx+5XPeEg3Yhc2KRcXJxZGXaavl2A/oCY0ooMbe0HbT3b951hCu8kxUYLfSJ5lTjBjkx6gPt992D4SgdTSur8ZPXT1LrYNRvWgJTkuPF9QtL0DdiLy5229lh3baYISK8Y69XppO3I1bmuZXfuJEQRo/0x+tA66QsJBJhtFuXWCcEiueBS0DADFx1kJRl+Q0A/ZqP/yuAf5JleWz6mbPTn98IYKcsy2OyLJ8CcBLAsun/Tsqy3C7L8jiAndPPXnZQL2S7Dp1B86oa2w4fTqZJZ5XVPzqODc8edsyJzez7iV2v3XdqnedIFsSZ4nTjtHMoYOx4xPr+lqVltuoQ4SRVmIWriuylz+4dCuK51ug59fDNS5S2uOHorFemngOgWUdOK/Pcym94+shs3fX6QS0HdlOnm6mXk7KQSM57Vuqi7jeSaVSNSl860lOSXHHkV69pv/z2crQ0NWBWdiqOdg3indP+hAocIHB5Ix4OknMBNEqS9A8AggD+RpbldwCUATioeu7M9GcA8Knm8+WxqGiiQa11IFfyTSursfTKPFROX6ua3Wyc1KqwyspITY6JtoP1fuKWYOedVrMgJhqc1rYbXZObMamgIRSScepcAB39AWSmJqM4J81WrHIWinO8GBgdV8xcJAnwSEBdxcXEKU5p0dQmEaW5XmaZb53yU+dN71AQx3uGTWm8rcxzpzKWqvvIimlKLLSlZuvlpEY1kZz3zNaF1m/qTKPE2fP2HQej+hWAIyZSHo+EKl8mjvcM4+5fvEM1YUyEwAEClzdcd5CUJKkKwCvEZluSpA8AvA6gGcDnAbQAqAbwKICDsiz/x/RzTwD47XQxa2RZvnf68/UAlsuyvIHyriYATQBQUVHxuY6ODu0jMxpuOJ+4bbOtl42OlZXM6gLMshl3IhuiXhbEWKdUvlxAG89Nq2tQU5yFVfOKHe1rs8l0rJIi2nu237kUs31ZUWYwejJHyIv6cz3ZtjLPnchYqu0jKw54bjpCkrqe9gfw/qfn8VzrmYjwjEZ9SmtnLJ204w09Gb0wMYX0lCSqrO7Z1IiPus0dGK3Ug5gQxsJpVkAg0TJIngHwvBxm+W9LkhQCUAigC4DaRat8+jPofB4BWZZ3ANgBhKOROFzvuMNI62BlkXdSq8IqCwCXFsgu8Ve/v3coiIkpGQ+8dFQh2nauZlnatRM9w/jxH9owMDruakrnyxE084Gte9vQtLIa1YXWtPOsfuadB3ZvBmht2vDsYexuboxyhmNpT8enQqY1vVbmudW1Qa+PrGip3bLLNTqc8/Sptp2JHJLVDbDGkzh3HvjkHON2ZoxqGlRmUXlhZEJIfCDEGisQL8SDbL8I4AsAXp92gEwFcA7AbwA8K0nSIwg7SNYAeBuABKBGkqTZCJPsrwO4Mw71TghYjRygR+bUZdolfaz62UkdbTatvbotT961zJGrWdaV+pnzF7C+oRLPHOyw7Fjq9O3CpbKhsDbQkBz+zul+joVDq1nHOtq8Oe0PWDL9stI+p/vEimmKW2YWLIfue1ZcdN6Np8P5TIBZB2nyfWB8kjoP9h4/i217T5peA/VMCL0pHpTkeC+rQ5BA4sFVB0lJkn4J4ACAeZIknZEk6R4APwdQPW1OshPAt+QwPgTwHICPAOwB8B1ZlqdkWZ4EsAHA7wAcA/Dc9LMCKhg5U+35sMcwyxbvc1bA48jkpLMm7zt5QXMcal5Vg1+1nsG2fW24pa7ccl2dyIQHuDt+8QDLCc0jARmpSabLc6qf7cCsYx1NhhPJoc4srNbdyblMwFpv0pI9CeFwPhNg1UG6siCTOg+mprvO7Nxkrc+vHOnCI7ddg6kQ4j73BS5vuKrZlmX5DsZXf8l4/h8A/APl890AdjtYNdcRaw2j0SLPo22Jt1YmkUJgaUG0a767l2H/yXOQ5YtZEIFwJkOrdbXqAKaVMVnmG+eZgipfJh6+eQnuU2W33LS6BhkpSZiYChkXoEGsw5LR4JRJxLzibPz0G3XITEtGcbY7TqNuwIyW2u01NCM1mbreNMwuwNraRkvvK8qmr2GzsuK/hrkBqw7SQLRpITHhITAzN7UmhBmp4TVizeISXWfjWM59gcsbIl27CzDrbOXEZqJHVHlJRrzJSKLHTPV4JMzKTqNmLPRIsFxXK4cMmoz96GtXG47fTDIz8Xgk1FXkYdPqGhRkpCIjLRlnBkbx8z+dwpN3LTNdnhOHOSfMrOyYRLDWloqC2MwRJ+SHZeusLrciPyMihbkb1/7jU1NoXlUTldFU8sDyepfkQVSSpU2ra5Dk6h2yszA7xjxx8Y1MC9NTktC887CivADMz029eiSyIkfg8oAg2y6AR0PstCONEVHlWWj0FqRYkLRECoHFAq2fH755Ceoq8ixrF60cMmgyZpSUJRbOW07LSUVBJip9mY4cwOwe5pzqPzt20PG8fdKLNtQ9aH28aeXuWF/vejtTk5LQ0tqphHqUZaCltRN/NsdnuczuwSCePtARUebTBzqwtCKPK9SlHTgx92Lp4Kn1r9m8ZoFripZEV+QIXPpwPfRfvFBfXy+3trbG5d0HPjmHOx57K+rznU3LlYgDboSz0gtFxatppz33xXlFOHi6H60d/QjJwMvvd13WsUudCP9GNsWM1GSMT01hVlYapkKICP8GsOPQ0mSsNNeL//7/zIswu1CPs5sh1Ei73Nio7fa3U2W53X884Flb3AKr/U0rqy05temV27x6DrbtPRn17C+/vRzXXmW9neq5l5rkQWvHAH78h48jNNvLZuejvsoa4Y6XjDg19+Ip407O83iULyCQaKH/LnnwXFm5YbLB0piZCWumfa4iPwOvftCNzbuORGxIW/Ycw/yS7Blr70bTAgF0ckt71ugaXM8OlRZuTJt8wWjzpMnYwOg46irymEl2nLINZ7XNrtZVLyyfUxEx7JQVSzMrVl/E8zpcLzoM+bcV7TOt3JBMv42bmJIRCsmWw2tq59R9X56PDV+Yg+BkSNFsr1lcYrpsgnhpUJ268YinKaHb0YBiEW1IQIAFQbZdAM+Ca3fTdNqujvVce9+IQrSByPBYvAtwrOyE7RDeR267BqnJkpJWnnx2/YJiQ9tRM1olvXBj6s3RaPNkyVh5XgY6B0ZBu7Byyjac1TY7G/VMiE8cK6Kr1xdWyZwTc9AoQytgjZjRyn35/S48eONiPPDSBxGH0gdeOoon71rmmAnOw789jqaV1di+76QjxDhepnBOkWRh2ywg4A4E2XYBPAuunU2zsz+AdzvPM80FnARrEU/ygGsBjhWJskt4v/fce2haWR31WUtTg6HGyIxWiSf5QvWsLMPNk3ULoXcwcMo2nNU2Oxu107bIbhzweFKRO/FOo74wS+acmoO09m9aXYOnD1yMIMEz3jRnSG25t9dX4MLEZITtM4n+Y1XLyppTNUXZ+Jvr56KxphBLyvJsy0k8NKhOkWRh2ywg4A4E2XYJeslnyEYzrzgbezY1omfI3KZ5vGcIO95od4yY6IG1iNdXFnAtwLFy6HKC8GrDUQcnQkomOe3n6g3fjFbJKPmCUTII9eZJu4XQ6wMrWjczbbOzUTt5fe3WAU+v/5x8J89BywyZc2oOats/K8uLU/4RDIyOA9CPmU3WPX9gDJ+dD0aYpZEbJGL+RCJTfPXqMjzxZnTkH6taVtacajs7DG9yOFQca6yccj5064bPztzT1ks9Fk5kKRYQEBBkO6awuyGTTfPexmrqZqzNqufEwkhbxLesq8V11T6usmJlA+gE4dU2x5viQWluuiHpNaNVovUnsdmmJYMws3ny9IFZomambUZkVE8Wnby+NiKXduYFq/+cPFQ6fZXv5BzUtn92YSaTmAHhNejUuQCOdQ9NR8tJUsLikXp877n3FAc8dWSKLXuORYXms6NlZc29Zw52YGB0HC1NDdTfOXGQctt5uHcoiIWl2Xh1Y2OEk7VR2Xr1oimKEt3US0AgUSHIdgxhd0NWb5q0zVidVY8sjFv2HMPa2jIkeYDPVxbg2mofkpP5g77atUGMlQ2gXcJLbLZJGeSzRaU51Gcr8jPQ3jeC3qEgSnO93MTYKPmCUTIIvX5n9UF6SpJlpzKzpJ8VQ3nfiV4cOTOIkAwkScCS8lysmles1MnJ62s9clnly3SFMDhJaJ2+yndzDuod3mjk7IG1C5GfkRoRT5l2IFyzqATzS7LRHxhDS1MDRsenbGtSPR4J1y8oxtN3L8MpfwAZqcl47I1PlLqMT4WUOa1+lxMHKTdu+Jwgv2bqFe+kZwICMxmCbMcQdjdksmnuOnSGmoxBnVWvsz+AM/0BNK28Cg++8lGEVvqrtVe4GhdYrW0xQ0TtwAxBYRFZAFQtHY9t9PY7l3Jrlaw6q1rpg+ZVNWjeedhyqEYnHL46+wNo6x1RTJ+Ire+cWVlK7GEnHcv0yKVbhMFJQuu0k10iRch48JWPlFCBBLR+csPuORSSo+Zt86oa9I2MIzVZwmfng1j/xNtRxNWJg5QbN3xOyLKZesU76ZmAwEyGINsxhN0NWb1pPnOwA00rq1GRn4GeoWBEyKpQSMa7necxMj6FR/4QeWW7edcRLCnLdW1xpGlb1ER0VpYXSR7grVN+R23+zBIU1mbOCp1oZBu94dnD2N3c6HqsYz2QPihrasDe42cxFbroVGaHUNolPr1DY1GmA1v3tqGuIj8i0YdTBEuPXLqVttlpQut0uEPagdFt21sWOavIz4i6QYqFAx4rElDTymrUVxag6ZlWKnF14iBVnONFpS8da2vLIE1388vvd9m6XXCC/Jppm4hUIiBgHYJsxxB2N2Syac7b2IhjPUP4uHcY//LaxxgYHY8o57Q/gPteOMq07XZTE0Hb0AgRXVblc9XmzwxBsWO3m8gaHo9Hwuj4VFRCkHjWLzA+Se2v0fFJR9/D43zMIgyzsqIJg56M0L5L5Oyn6rlhJsmVHULO6uu+kTHsWF+PlCQppk52rHm79Mo8pCRJzDm9rMpn+yBVkZ+BjatqcP+LF0MZPnTTYlTkZ1hujxPkV29P4okaIyKVCAjwQZDtGMKJ62GPR8JVRVmYXZiJhaU5uO4qn27yklhrIvSIKICEsPmza+uY6Boes/VzO8JAZUEmtT4VBfY3aaMoF9oxZYWvO+UfwezCSDLNkhEAuk5l8T5wGYHH/MAJe2BaXz+wdiECwQmMT01hxZzimB5GWPOicpossuaMlXVbO6dkGQrRBsJ9fv+LH6CuIt+yvDhxm6JnUkcbf71IJQICAmyIdO1xgNvkhqTczc9IxfqGyihvfje9x/XS/fYOBeOWapq3jrxa8Xh45dtN2kOrXyza4mYkBlLuPSuqqWHiaGN6+twInj/chZAcjt/8/LtnMDA6HvGsnowAiHvadjvgSfludo6wZDMUknG067xi1vT8u2fQPRi01F92102rBygrIf60Zf3oa1djwy8PRz1rd+3jTUFutu/imbZdQGCmQqRrTyAYLfhOkHCabffc4mxU5KdjbCqEzv4ApkLA2eHYx3tNBI2wXTOQeGSJM0NYeetHiNDxniHc21iNXYfO2LbvpsGt/lJraElSIDVYY9o9GIwyswHAHTtdlvnflYjgufnwB8aUhDIAFNmgtdFINp0wa3LiwGYkh07JaGd/QJlTQLjvwmEPnV/7eEznrPRdIpvKCQjMRAiyHWOwrnAXbmrER93DjmhWWMknbttxEPkZqfjmtZWKw5obdtOsTStRspM5YQbiRrQEPZiNPMAKwaeOEqOVNxJz2E6WPhbc6C8tIeAd06Js4/E3kpFEODSyYKTF5MmE+dn5oHJTQGSjpbWTattuJJt25htpS9/wmGOJeVhy6ISMEud0deSd5lU12PNBNx6+eUlU1t94OYYa9Z3RmInkNgIC5sAfcFnAEbA0Br1D9M3ktD9g6T1k42ioLoQkARuePYzgRAi31JVTk0qYfU8oJKO9bwQHPjmH9r4RhFTpF9XvJhnvyOdrFpVgd3MjdjYtx+7mxrgkRCBkw5sSFv9EdPTR9q+RLbxRGZ+cHcG+E724Ydt+3PHYW3j+cBc1MsMtdeUJRRz1QAgBACUcptGYhkIyTvlHsGm1/rN6MpLI8kO0mGScb9i2H3s+7Iman3rz8LQ/oNi+Axdl43+sWYBT/si5Dhj7aVjtr8nJEN48eQ4vvteFs8NjyM9IZb4jUUCc07V99xfzi1BXkReXtc/K2qE3ZjwyJiAgEAmh2Y4xWBoDVsQGJzSM6sXWzHU7C3audGOtEWbVIZEjR9D697H19aadHrVlbFpdoyQUCTFMIZI8SBjiSINao1aU7cX2O5diw7OH0T0YDn9pFOXitD+ADc8eRn5GqmIm4ZGAhaXZEc/qOY6d9gcwKzvVsWQrToJXi6k3D1nkrO3sCH76x5NRdrtGWlAzZk3qsW07O4zvtkTK79MHOpQkNDT5j7fGldV3c4uzUVGQqfR7LGHVYZol47TQp1ZuGeI9VgICsYQg2zEG6wqXFbHBCQ2jdrG1+554ZhJzaoFOBNLPAq1/73/pKLasq42KtsEixbQytu5twz0rqvGT18P2szQ5WD2/CEvK8hJy02Md8mgh/lggZKh7MKj0AwBcd5UvIuY3EC0jrPcvn+1zNGSeHbgZe3lsMkQti8c8zGi+GR0OifyShDi0d6jLyM9Ixa315ZhblI0FpTkRkWbMwOxYsvpuQUlO3OaUGfM9Hhl3QsZE6neByw2CbMcYehozt+yZ1YvtrkNnsGl1TZTNtpn3xMt55nJZoNX9W5rrxS115ZAkoDw/nTtLJWuM1A5vNDngJdrxIJSsQ56ZZEJ27IdjFTLPDLTjUJpr3x+hIj8DD920OCImNLHnp5XlxE0Rz+EwOBFCeV46nryrHpXT5jzqd5AynIrCZGUsWcR2dqGzN0Vm5p+Z8eGRcSd8XkTqd4HLDYJsxwEsLY/dWK56z88rzsZPv1GHzLRklOSk4fqFJVykjfY+jyTFxEGMFqvWyQU6Ua8xyWY2tygLf/UXc3C8Zwih6bbzpl1nbYjkZwOj46gpzuIm72rE69DD9nfgP+TZcdLlOWTGkkSwxoGY1lg9THcOjOLRfW3Y8IU5KMnxonNgFM8c7IhKnqWG3Zsio8MhEJbfktx0rJhTSJUzUsYtdeUK0SblWBkDK2MZCxM1K/OPd3x4ZNwJR3cR7UTgcoMg2wkEsxkQebPAsZ67qsicfZ36itasdtwssWXFqnVqgU4ULTmtX6p8mdh+59IIp1miXdyy5xjml2Qbtpe1IS4szY5KhGRGDoD4aaVYB4iJKRmhkMwcN20fW03MwaPRiyWJYI3DqxsbbSUe6R0KosN/AT/6/cfKzcq6z5WjcU4hPl9V4Pj8CIVkZKQm6x4OvSkebFlXi+uqfcz3k/Fxwi8FoI9lfkYq+obHdNcxt03U3Jx/PDLuxIEi0RODCQg4DUG2Zyh4F1ynFmZ1Od2DQTx9IBy/e+mVedQrXTWsEFtavZ2MVXvqXPyvMfX6ZbYvS9FOkvpt2xe+VuchDbohGAvttS9eWqkqX2aU3Xrzqho88NJRPHnXMmayFb1sj2bfb6TRiyWJYI1DZ38Afz63yPJYqNtAbNu9KR7csrTMFaK958MebNlzDM2raqJMP2iHQxYq8jOwZV0t2vtGqGOQnpKkeyjT1mtySo4opzTXi29eW4lvPfm27QO6nVs1p+Yf66DPo7XWO1DwtC1RwsAKCMQKgmy7CDfNFHgXXKcWZm05JDHIzqblhuVYIfy0ej/XesZWrFr1eJwfnYj7NaZev5wdpo9bkiccJ1orWxX5GegcGI2SNTc0bPHSSnk8Eq7I8ypRRGQZhnHBrcgea97yaPScJBFG6wdrHA5/eh4XJkKWb2liSYTU4/PMwQ7cs6IaSR5EOOqqD4d6mSp/f6wXj7x2Al//fAUeWLsQD77yUcShrHnn4SgzLFZ5p/0B3P/S0YgDwK319LCpVqJw0A6AC0uz0T1ovFewxt3sYYJ1CGXJeCgko7M/gN6hMQTGJ1FZkBnleMqrWEn0iFACAk5DkG2X4LaZAi/hcYoY2SnHCuGnvW9gdFyJVUtboPXIiXY8Nq2eE/drTL1+YfV3fWUBKvIzItpS6UvHxlU1EQ5tbprEOEHGrB5EfZlp1LTsrHEzK3tG89boAOMUieBZP2jjQBwZB0bHLd/SuEGEWOOtHh91hJjrrvIBgBJjnhwof3+sl9onatK+Zc8JlOZ60bSyGuV56ejov6AcytTkWK+PiSkNOQBIElCWm+7IAZ11ANRGWmHNX9a40w4TZutA+oaWEGvfiV609Y7oJkQzc7hN5IhQAgJOQyS1cQmsRcdqkhoteBNFOJWAw0456uQjBEbElvW+ioJMasIco0QL2vF4rvVMVGKTh29eEtNrTL1+obWf2Kx2DoxGtGVtbZlCtAHnZU0Lu8mJ9MZKL1kSYF4OS3O9aF49BxtWhf8j0TpYsqc3b7V1m5wMUevKSurE2zftfSN453S/4fpBxuGpu5dhw6o5uGdFtUIqCQm0CjttoLWJNd6sOTAryxv1m1c/6MaWPceofcK6eescuICfvH5Sic2t7he9sSb1IgeA7ftO4rPBC6bXMRpYB0Ai6kbzl4x7S1MDmldfHPcO/wXuea+NePSdL8zBvY3V6BsZoyanOe0P4MiZQcOEaFYS6AgIXA4Qmm2X4KZdK9ES5WekoKXpWkxMTaEgM43prOOElkqvHLvpoc2+jwYjjQptM376QAd++LWrcaJ3GB4JqKugh72jtY+8046JkF6/6LVf2xanHMLMwI5WijVWCzc1RqWQ12rOzMhFKCTjo+7hiNTZm1bXoKY4iyl7rHnbHxjD8Z7Iuj1002I8uq8NHf4LymHoijwvfIy5aAS1pvXexmquMfV4JMzKTsPj+/m1/bGG3tykzYEt62pxfnQ86jebdx2JCAVIPte7CdIOgbpf9NboZVW+qHotKc91xLyGVVdZxXGN5q/HI2F0fArb9p6M+Jx33pM6aMMkPr6/napV7x1iJ8JSv084PgoI0CHItktwYtFhkTza1WddBTtKgFPXdbRyeK67rRJ+M/U2OtywzFKO9wzjiTfbFa25Fqz2pSZLUeHVzJptGPULq/0s2aLJWiKGN2SH8BvTPTAR8MoFK3bzqxsbTdvDpiR5osq6/8UPFPKnJoNEnszKg7a+vOtHIjib6cmZ0dwkc6B3KIiJKRkPvHQUX726jPqbJM1drPYmiDZPST9q+0VvjWbNTQC2FRe0upLsmNp66MHOHkPqcLxniCtMYnGOF0mSsUwmgiwKCCQiBNl2CXYXHRbJm1ecHfcoGmo4kR7aCRhtPCwNWlmeF+vqykwneWhaWe3IGFjpF21bXn6/KyoJySO3XRNl2+22LTcvWGPVHxg3raG3QvL6RoLMcIeseTs6PkUtSx0HmvwdnAhhy55jKMvzmkrnrq7vrkNnqNE5WDGu4+lsZnTg5knnTsb3hm37dQ8bdRX5VPKsR45ZPh5GazRrbtpdx7R1nZXlxSn/CAZGx5V2bllXi4r8DN1y7OwxpA7aPgboc67Kl4m6yrwox1NaplDh+CggEA1Btl2C3UWHRfJ++o26mJsMsBAKyegbHsO9jdUAwgRBbS8ay0QzPBundoNL8kCx5WTByL5S/VmsxoAmW+W56agpykL3YBCluV4sKs2Nsu1242BmZbxYB58Of8CUps4uyaOBNW9PM+qmvvonf9eW5eD2ZRW4fcdBU4ccbci9Zw7yh9eMp7OZ0YGblxTSDhstrZ1YW1uGJA+w9Mo8PPvWKSViSX1lQUTcbbPkOJ7EUFvXyoIM7Fhfj9aOfkyFgEdeO4GUJI9hoho79fd4JFT5MrnnSGAshB1vfMLsf1bbBAQEBNl2FUaLjhWtXGYaPfkDbXF004SARnTUkRCs2OiZSdRDaxevTXl9RQEzqoG2f6zYgvK21c7YqGWL1W/5GSmuHsysRtyhjZU/MIZ/+u3xKG2untOqUySPVj/tvKWVRWy2ASjyv+eDbvzVX8yxdMjRvmNgdBzzS3Lw53OL4qIZ5JVRIzMRXlKoPWzs+aAbTSuvigrhR5xAvSke7LZ5cLSzRhvBzG87B0bR9ExrRD/yyIxdYss7R9RzjdjMO9H/AgKXCwTZjhOsauWKs9O4Fkd1sgiiGfp8ZQGurfYhOdl+EBoa0dm2rw1NK6sxvyTHko0ej0mKUb9x2ZTfeg16hi5EaORZG5sVW1AjOB0WktVvLU3XuuqsZCcxEG2sBkbHI0Kt6TmtAs6RPF6kJktoWlmNkByu26zsVPz8W8twdviirfHa2jIc7xmydMgx6/zppi2+mYOvNvkLAFT60pGekoQDn5xT6mdECrVz7S/mFylEG7i4xqjt5N28TbIzT3l+qx7DCxN0MyW3b8t4ZU6kVxcQsAdBtuMEq1q5ioJMVBRkGi6Op/0BbNlzDLfXV0RoCresq8VXa6+wvTGzFt+lV+ZZ1sTxLOhmk5Ron8/PSEVHfyAiVizRltE2Diu2oEZwOt0yq98mpqaiZGj7nUshy4ggQVbGKhSScazbGqmkQS3vJGMhy2mVgDe1NJlPp/0BvHXKb6ndp/2BiIye5F0tTQ1YPjscE/rJu5bh495hfPDZkOVDDo+m0u0Y/oC5DLXa5C+VvnRs+EKNaTMa7VwzspN3O8qFnXlq9FvtGN735XloXj1HMU/bdeiM5RtCs2DJnPowkJGajEpfOjr8F5TvRZQRAQF+CLIdJ9jVyhltyL1DQaytLYvyNN+86wiWlOXa1kawiE6lDQ0bD3kyq2HRPn9LXXQWOKKRZ20cTjtKOa0lYvVbQWYa6ioKFBkqyfHio+5hfOXR/VzaNj1SetofQNvZYcc051a00LxX4E6QU9aY7T1+Fl3ngxHp3//xt9Gpx7esq3UsIoPThzUtiAzwyCgt+cvc4mx8/9fvR9Vv3sZGSBJ0ZUs911hp12XZer4AM7AzT41+qx7D0lwvZEi6ISpjHVWINme0oS5FlBEBAX4Ish0nmNHKWSVgSR64dvXnRognnjLNOr1pn2fFpJ5bnB2zjcPpWLTMW5D8jIgNGoApbZseKe0dCuK51uiIGXYSA7HMgFgkg5egO0FOi3O8qPSlY21tmaJZffn9LkyFEHUjtXnNAmzZc8zQkcwq3I7hv+fDHpzo4dPOa5O/AEDz6jnU+h3rGcLf/Op9Q7MUMt6luV5LEYScgt48NYq9n5Gq71ujHsNb6srx4z98HCGf6hCVZg+LThBz2py5/8UP0NLUgAsTUyLKiICASQiyHSe4HY+0ypeJz1cWuGKzayapjt7vtZsBD3ky229VvkxsWVeLzbuOIDgRYsaKXVCSE7ONw+mxp/UbLbX1wzcvQX5GakQEFpa2jXzHIqXFOV7TNtZq8BAC3hjuPLc8LHJKzEuMiElFfgY2rqqJCK/4g7WL8Mu3O+g3UiXZrkW4cDNxCJGB/IxUrtCDNFlmrTsf9w7ryhZtvLffuRSvbmxE30jsw8ip25afkYpb68sxtygbEui5DtSx9yt96Uo4TvVvZRkRmTOJWQxNPkmISjPz0ikTI9acuTAxhYbqQmsdKiBwGUOQ7TjB7bBTHo+Ea6t9EUTTCULPWsxJUh0jEmW0GRiRJ7P95vFI+MriUuRnpKK1ox/elCR870tz8chrH0ds6IB9O2YzmFecjZ9+ow6Zackozk5DRYG9d2r7rb1vJGqDvu+Fo2haWR2RdY6lbSNgaUyt2FgT8BICp8wlWOSUpATnISadA6MK0SZ1+ftXPsQ9K6rx8dkRx26keODmQZ3IAAk9SA5SjXMK8fmq6MRZrIOetn4P37wEP/zdiYjfamVLa1pxS105jpwZxKwsL5ZVOXczwAvStoWbGvFu53nc98JRBCdCaF49RzH5IO3Qxt7v8F/Ao/va8Ou/uhYf944ovyVjdf2CYqWPAP1kMWbmpdtzRthoCwhYgyDbcYTbm7LHIyEzLSkigkJqsr0Nyyj1shF5cWIzMNtvyckerJhTiPL8dMV2efX8YvSN8NkxOwkW0eQhqeT3dsKxzS3Ojoqi4pHCBw2jq2817BwWeWXAKXMJFjlNTgKO9wxxRaVh1SXJAybRdcvO1s2Dujb8HjlI3bK0TPdAq52P2vp5JChJWwhY/hilud6IFOI73rCWkdMJeDwSQjIUsgyAmbZcG3u/w38BgbGpiN8SWd/d3Kj0UX9gDDVFWUyliBni6/acETbaAgLW4CrZliTp5wDWAjgry/JizXf/DcCPAMySZfmcJEkSgK0AbgAwCuAuWZbfnX72WwDun/7pQ7IsP+VmvS8VsCIo2ImNqreYA/o2wUa/d9K5i2aioiUEVxVlUTXATid+UcPKYUPdpskpGfe/dDTCSclMfPAFJTlKFBWSuW7N1v1RV988G6zVwyKvDPCQDB5Cy9K+/ubIZxFOaSROdt/wWFR5rLqsnl+EJWXRpjOsQ9X1C4rROTBqm4Dr9b0dku8UydLWLxSSuf0xbqkr50ohHivQ5JUmC9ou9qZ4EBif1JV18l9dSMaSslxLmS7VcEoj7fbNq4DA5Qa3Ndu/ALAdwNPqDyVJuhLA9QA6VR9/GUDN9H/LAfwbgOWSJBUA+AGAegAygEOSJP1GluUBl+s+4+EGsWUt5ukpSfAHxgzfp3el3943YouEWLFXZPVR75A78WPNjole8qDuwSCThFTkZ1BNiGYXZipEqL1vJOIwRq6+3XaC4iUERiTDzHjTzGy0GseW1k40rbwK33ry7ajyWHWhEW2Afajasb5eSV6iLp/8xi4Jt2uz6ybJMjKdIn1sNU45ATls+ANjSE3yYHR8ylafEnnNz0jFLXXlSE3y4Me3X4N/+u2xiEMvLfZ+ZQFfhka9w5OZMXFSI+32zauAwOUEV8m2LMtvSJJURfnqxwC+D+Al1Wc3AnhalmUZwEFJkvIkSSoF8BcAXpNluR8AJEl6DcAaAL90s+6JCjNaKzfs7miLefOqGjTvPIzvfWmeYSxW1mZwyn+R+Fk15bCiNWb10cSUjFBIjhvRJGAlD9JL7BEKyfj9sV488toJ3YgYauJPbGQlCQhOhly1keUlBEYkw45JEu3Qs7a2LCqJiro8MySUdahq7eiPKn/exkac6B12JG52PMy0jMBrOkXGuywvPcImGgjPkZIc4wO5OpmXNseA1T6t8mVi+51L0dY7EhGff8u6WlyR50VqkgfjUyHMykqLcuYE4MpNgd5zQiMtIJB4iLnNtiRJNwLokmX5fUmKWADKAHyq+vvM9Gesz2llNwFoAoCKigoHa50YMKu1csPuTtkQmxqw9/hZTIWgaFk37zpC1dyp30fbDDwSFFMGwPq1MYvgfNw7DCCs7dVe4WujlZDDwwMvHcWTdy1zXKtjdkxYbdJL7MGbWlmtsVPbyD6+37yNrJlDoBlCoEcy7Nzc0A49RqEyzZBQ1qFqKrJ4BCdC6Ox3Lm62lT5xO4azmQOAxyNhSVkuNRnTR93GBxLyrntWVJs2RdEzQZvty4q4BQpOhHMWPP6teqx/IvomRF0nN8kvq85CIy0gkFiIKdmWJCkDwH0Im5A4DlmWdwDYAQD19fWyweMzDma1Vm5pOTweCaPjUxFRLUh9UpIkw8yK2s3gwCfnHDF3YRGco11D+G7Le9SkDGsWleCKPK8SdUGWLx4e3EhFbHZMWG3SS+xhRLjUG/Rj6+txrHsQj/zBuo3s5GQIr37QHWWyomef7AQhsHNzYyZknZWbIFr5W9bV4pHXIiNyeFM8yEhNpt4w9I2MmZ6vZvvEqtmJGYJu9gBAmyOyDMWJmfz+e8+9h7KmhghTHvIuVjg92hwozqGHylT3w9lheht6BoNKOE3WvPF4JGWO9g6FfVucWIedCPMX62Q5AgKXK2Kt2b4KwGwARKtdDuBdSZKWAegCcKXq2fLpz7oQNiVRf/7HGNQ14WBFa+WWloO1qRfneE2/zylzF3Lde+TMIEIykCQBBRmp+Nn0lfT9L34QYX5BNkZfZhqeeDP62lrv/XY2KTNjwiJteok99Ozq3zntx2fngxHE+KGb9ONv6yEUkvGndr9SHvmtnn2yU5u5lZsb9bjNK87Gnk2N6Blih6zTK89swp2K/AykJHmiyi/OSXPshsFsn1h12DVD8qzMb94DuTqDp9qRlbyD9k5a/Xesr9ftB1YbOvtHcUtduXKDxDLrciL2tRZm42/TkvC4US8BAYFoxJRsy7J8FEAR+VuSpNMA6qejkfwGwAZJknYi7CA5KMtytyRJvwPwsCRJ+dM/ux7A38ay3okCJ22w7Wo0nDRRcbKs8Uk5IsLEX39xrvKd2vyC/H12OIhlVT7TJCtWm5RZTXgoJMMjAQ/fvCQitu9DNy1G887DWFtbFnGwCB9C9ONv6+G0PxBhh0zAsk820pa7ZY5CyjYaN97yrCbcoZUPQHEMtBuFw2yfWDnAmyXoduY3kYcLE1PYtHoOnms9oxwKvSke1BRlY8ueY5hfkq1k8HzktmuwZc8xZlIeWv1ZMkz6oSI/Aw/euBgPvHQxUg9xVF73uXLlN0ZmXXr9ZXZN5h07lqwuLM2OaSQmAYHLGW6H/vslwlrpQkmSzgD4gSzLTzAe341w2L+TCIf+uxsAZFnulyTpQQDvTD/3v4mz5OUGp0ipE2SRpbmzQuCdMnehbWo//sPHijabmF8QkI3R7PudShzBC15NuHpc8zNS0bSyGnOLs1GRn46NOw+jw39BN129NpICj1z5A2OoKcqmHgJp9slGtsNm5dLMLQHPuPGWZ1UGWOWvWVQS1YekXLPmTE7YlusdtJwwC+GZ3zR52LS6Bk8f6MDA6DiaV9XgR78/jtvrK9AfGFPs6kkGz/7AGFqaGqKikdDqH5L1E8t0DoxiIDCm5Cwg5mYDo+NKyD+rZl2sthrJPu/YsWT1qbuXOSJvAgICxnA7GskdBt9Xqf4tA/gO47mfA/i5o5WbgXCTlFohi+pN3YmQY3bNXfScCYl299F9bQCiN0YzcYt5QhzGA+px7R4MYtve8AHjqbuXRUWI0W7Q6vjbZsjQZ+eDeOS1E1FaxH+8uRb/ujfaPlmPxLl5iCFjyKsJNDo0smStwx+wNCeJXa/T0YOMYOUAryV5pble3FpfjtGxKbT3jXD5afCAJg9b97bhR1+7Gsd6hhXfim3T4Sp530UjqS+/36Wbbbd3KIgn/9QRYebjTfHgf61diMELE2hePYcZc52HFFuRfd6xY8nq6PhkzOVNQOByhcggOcPgJim1QxbtJmsx0oTzPMva1OYVZ6NpZTU+V5GPJ+9apksoeRyn/vHmWsMQh/EAz6a669AZ6vW6Ov42EO4HozBrp/0BhZyQ1N5JHuDaah8+d2U+0lKi7ZP1SJxbCY/IQfBEz5AhueA9NLJk7fCn53FhImQ5xFyss/ZZOcCr65mfkYpvXlsZERLPKZMqljwExiYhScC6z5Vj16GwWcno+BR3ubR+3rxmAa5fUMxMLFOc48XA6HhECnuPBAxemMDWvW26Mdd5xtWqPw7P2LFktaJAZIkUEIgVBNlOEMTSK9yN+NtOJGthbdK8z7JigD+8+xi6B4O47iofGqoLTZkx0Byn/vaFI/i3b9Thv/7nuwm1SfFsqt2DQbS0dmLH+nqkJElUWePtb/WYk9TeQJhsp6YmmSZxbsglcPEgmJ+RyrTj1T5rdGhkyRoxLbCijY9XjGSzB3iPR8LC0vABtqYoG//91++7chvBkoeuwQvYvu+k0uctrZ0ozomUEbPOq0Yh89TjTUzS/vHmJSgvSMfu5kbbvgVWZZ9n7Fhkf3ZhJmYXZoqY3AICMYAky5dchDwA4dB/ra2tMX+vEWnm9QonCRN8mWmGCyDrnXqfO+3g1943ghu27Y/aLFip4c08b+bZUEjG0a7zSgzw598Na7706qL3nubVc6JCHALA31w/F401ha5mWjQLvXEFwkSSZ1Pl7W+zY26n/nb69sAn53DHY28BiAytt3x2Aa7MT1cyGYZCMt453Y/9J88BgKI1BYCdTcvRUF0YVV/yvCxflDXW827BiYO62TJIn25YNQfb90XPDyfaHwrJePG9rghHX2KzrXaS3LG+HivmFCr1NStHvG2fnAzhT+1+tHb0YyoEvHKkC5vXLLAsn+r3luZ6ueKIWwV5lyDVAgLuQZKkQ7Is19O+E5ptB2G0yLO+n1cc7RW+edcR3LOiGk+8qR/+i1Xm9QuKdePGOq1BcypZS39gTPmebHxmtOYej4QlZXnoOh80fT1qxnHqwkQIFyamYkaoeGA0rrzaS97+Zo15RX6GoQkKT/1nZXmR5AHeOuV3JN02sWVXO8t+e3p+0eaLWlNN0zB6PBJmZafh8f3mwkY6CadiLVt1zgP0HQuttEdNfJdemac4Jc4rzlZuqQiCE+HY/up6mg2Jx4rU0T0YKb+dA6NKKEtyaDveM4SyvHQsKcs1JZu02PTb71walYHSKULsVghYAQEBPnjiXYFLCaxF/rQ/oPt9Rz/dcYtEjlCXwfvOD7sHdetCFl9iVmF3USdEaXdzI3Y2Lcfu5kbuzZqg0peOrvNB3LBtP+547C3csG0/9nzYg9Lc6Gf1NnSzddGr08vvd+Efb14SQSyaV9XglSNduolC2vtGcOCTc2jvG0EoFLvbIyfGldYPtP6m9TMhrdox5O0DUv9lVT6c6B3Gmq3WylGDHAq0Y/j8u2d058u2fW24tb5c96BGKzuWJkXtfSO685wHRusWDaTdL7/fheZVNY60nxBftex8cm4E80ty8Pj+dpzoHcbA6HjEb7wpnigTEr3DIm/bnz/cFSV3pNzSXC/WN1TiiTfbsW3vSdy+44Ap2WTFpt/w7GFIEhxbkwUEBBIHQrPtIIw0gqzvM9OSqdohYuGjZ/vMKpNkNGPVxQ2Y0Z7QtKIP3rhE0RyR+n7vuffw6sZG0448VjQ5LMepL84rgi8rTbk+bmntxOY1C6jv17tpYGVTjAf0rs7NaqzV/cwif2ZteJ2MTKLWmH/cO4yjXUNKJAtSNmu+LL0yD38+t8ixWN9OYnIyhA+7h2zPc1vOeToh9syCNuYbnj2MPZsasbu5Ef2BMdQUZTEjhhCYsX9mtT2kWnuJ3JFyb6krtxUP/bQ/gOPdg4qjJXDRZOnscFCJBZ4oa4WAgIB9CLLtIIwWeWbWxew0prOVtgw1QiEZ2d5kbL9jKQJjkzgXGMN/HOzEwOg4SnPTEzqsE42ksDa+vpFgTAiNHnFaMacQ5fnpODscZGZuBOiEYcueY5iYCkWRhHhlajMyG6D1g1E6awKnIopYLYd1iFBHWvluy3sRpgBJHmBWVhp1vlRyyFm8rug/7B5Ee9+I7XnupnOeGbDGvGcoqGh760IyM2IIgRmTNlbb1a5MRO5I8qvjPfYOOP7AGDK9KXjkD20R631LaydmZXlFVkcBgUsQwozEQRhdKbO+ryjIVK7jf/nt5dixvh4trZ2KYx9towiFZOw70Yv9befwN79+H5ufP4pH953E3X9Whe13LsWi0hzdusTT1IFAa/KgZ77gtNkLb53UEQx43k8jDGtry6jpzM1c9TsJHrMBbXs7B0a5TA14TVCMYKUcmhmC9nqfzMFKX3qEKcD3fvUeHrppcfTcnNbmOz1PnJh/3YNBPNd6JsqM46GbFpsy44i3KQwBz5irzYyAsD2/tv/MmJHR2r5pddjESFsHUu7q+cW2ZDw1yYMHX/koymTp7/9/i5HkAXOeJcKaLSAgYA1Cs+0gjK6UeR3YQiGZGQ+aaO76hsdw5MygkpocCC/Mj7z2MV7d2IjkZA/zXU45VTl91RmPOMNOg6YpS/Ig5iY9euNjRWvcOxREfkaqEskDCF998zpNmh1DK+XwZohcs6gEZXle3L7joPJsh/8CHp1OjkIizPBq883Cqagrpbnp1NjP80uyTZUTK1MYozWDd8x5+o9X605zyj3lH1Fsw7V1CDtg59qS8dHxKer8S0v2MM2Z+gNjON7jXrQSAQEBdyHItsMwWuR5NgHWM+pN5t7GagB0Etc3EsRVRVnMcnjtYWMZOpC0O172r06BRhg+X1kQU5Meo/GxYjZQmuuNSl6yaXUNSnLoTpN2x9BKObyHCI9HohKeDv+FiAgzTtmfa+GUPfqi0hw8dNNi3P/iB0qElYduWoz5xTmm6+S2KQwvQeYZc6czjWrbPrswUzebql0ZZ5oTTs8l2ncpSR5XZFFAQCA24CbbkiT9GYC/A1A5/TsJ4Szr1e5UTUAL7SaTk5aE5tVzFGeeXYfOYGB0HCU5Xt3QazykRG9zdDOtNs+mrz4EFGWHw8Npw3TFCyx751hq7I3Gx4rWeCoEhWiTMne+04nrqn048Mk5qn10LGRBDTOHCJ5n3cpoaSbspZ4sJyd7cNPVZagpykLPYBAluV4sKs1FcnJ8rQNph3TeNYNnzFn91+HQTZsVhQhPxlUCo/lH+46lDXfzdkxAQMA5mNFsPwHgrwEcAsCfG1eACbOmGOpN5o0TZ3Hn8ko88ocPIzSNC6/INkyOwEM09DZHXpMCN0A7BJBEFwOj4wlxtUrbrGOpsTciiVY0c2eHI8sszfXi9voK3P7YwYS51jZziOB51q2MlrRySdjLv3zibd3+pK0ZV1+Zj6uvtFUlx6Cdn5W+dDx44xIEJyZxb2N1RKIgq2SRNS6HPz2PbXtPxlwWzd70Gc0/2nen/YGEdngXEBDQB3cGSUmS3pJlebnL9XEM8cogyQtaUgOjDUKdse87X5iDJ96MTqbR8u0GhQCpP1dn9ePZHNRZ99T45beXQwJw+NPzUSYFX15cgqpCd8k2K2vhPSuqlat0vQyGbtia85bv9rsJnM7sSCuTJX+vbmzEVUXx07SZyZRn9Kxb5lK0cnesr48IewlcHDN1KLjJKRn3v3QUHf4LMSGVZmVWLSckHjUJk6eOssSb1ZVVJ9aBW51Z0o68m4F2bpTmenFrfTmuuTIPVb5MR+a5W7IoICDgHGxlkJQkqW76n69LkvRDAM8DGCPfy7L8riO1vIzASmpgZIqh1saRhDdqBCdC6Oa4+ubRbLK0RxmpSfjTJ/4ok4Kte9tw/cIS+51jAJbWlmjY9bRlbm9YeuWHQrLu4cpJIu6Go6m2TJbT57GeIcwudO4QYbZfzJie8PhX2L2RYNWfN+xl71AwyjFOTVjdtNu1Ml/U7aDFo962ry0iM64VmdT2nwQJ3215LyqzZKxMLNRtph0whD+LgIAAjxnJv2j+VrN2GcAq56pzeeC0P4DWjn7TNnjqBbdvZIyaJpo3vrYR0WARtvGpEAIM+8HO/rCW0E2tbVG2flxcvatVp2zNWQSKVf7CTY3o7L/APFxV+TIdPQTobcx2SP3C0mw8dfcyTExNwZsSXjpC8sWEHN4UDyADf/z4LKoLMzEVgi15SARtnh37c6P6a8ulHm5TknD3c+9QCetPXj/pKqm0Ml/Uh3SWQqC2LEfR2DthV93eN0LNLBkrEwt1m40S3tiZf247sQoICLgHQ7Ity/IXAECSpGpZltvV30mSJJwjKTBaUHuHggjJ9M2VN5EEiwyT+Np2tZosR78PuwcxvyQ7LjaToZCMU/4RbFpdE2XC8vSBDnhTPNiyrhb+aUczK46hPHVgESi2dnJM93AF0GPr2tFY0jZmq+RV/bv8jNSoqCQkIcdfrZyDH/7+OMYn5ahnrMiDm464sYCZ+rPm89DYhO5Njpuk0sp8UbeD1E+7TpTkGpNMM6Q03iFDeW4cSWbIeB8eBQQE4gMzDpK/BlCn+exXAD7nXHVmPngITXGOFy+/34XmVTUR141b1tVybxB62ks71420TY5oZNSEi0V4AfdI0Wl/ABuePYz8jFQlrnBmahIaawpxzZV5mJiS8YCOPasTDm96BIpVfmB8Uvdw5VbUCzN113uP+ne31JVHmRBt29eGf1//Ofyvlz5Ah/8CvvOFOVHPqLX4vCQqVv2ihpPmPGbqz5q375zuZ97kWCGVRu1Tf5+Rmqw7X4xMZGjp1ZtX1aB552FsXrMgYm5qIwyd8o9gw7OHuUhpvE0seG4ci7K9M/7wKCAgYB08NtvzASwCkCtJ0i2qr3IACFdoDXgW1CpfJjavWYAte47hnhXVSPIA9ZUFuK7aZzoZBe1a0ex1o3qjYzlgqdvVPRjE0wc60LSyGuV56UhPTcbDu4+5bjNJyEv3YBA/ef2k8vnSijwU53gjnJRY/W5WA6YlFP7AGJNAkXTO2vIrCzLx9y9/qHu4ikWkAavkVf07luauZzCIDv8F3Wd4EnOYIXtOw2mzFbOHO9q8Lc5JizrYPrB2ISoL0rGuzpwphlH7aJFESBxv9fMV+Rk4fW4E73aex30vHNU1kakLyajyZWDv8bOYCoFqa85yeMzPSFWSvBiRUrMhQ502ddO7cXz45iXwSEDfCHvtEGRbQODSBo9mex6AtQDyAHxV9fkwgG+7UKcZDR5Co2hCSrLj7uxC2+hoDljadnUPBrFt70lsWDUHAGJiM6lHXkz1O6cGjNY3W9bVotKXrhBLdR1Y5QPQPVzF6hrcqmZf+ztaGbMLMw2fMUrMwUv27PaLWZt7q5pHJ8a1oiATNcVZaFpZjZAczhBZnJOG666aZXq9MGqf9nslq+a3GzARCiElyYPA2BQOtPvxwWeDzNsLWgKhbXtPRtRFPTdp9dq696JduvZ5K4iF/T+Rq1nZqXiuqQGdAxfwce8wfvi7ExgYHdddOwQEBC5t8NhsvwTgJUmSrpVl+UAM6jSjwUtoEsXZhbbRaR2wiMabdZ39/LtnorS2TpFF7fXy9juXRl0vm9EOm+l3Wt9s3nUkIkybtg6s8vUOV7G6BrdK/tS/23XoTJSm9ZHbrkHdlfmGzxgl5mCSPVUKdbv9YsXm3irJc2JcPR4Jq+YVo7owy7ZsGLWP9n2H/wJ6R8YwOSVH9NmDNy7m7iujNdEowpD2eStw24RDK1fNq+dgxxuR5iRGa4eAgMClCx4zkkcRjjoCSZLu0H4vy3KzC/WasYi3s45ZGG10JNxf887DUYT6gbULsX3fSXQPBvHMwbBZydIr81DpcmzZPZsa0TMUSTzc6HdW36QkSbrpnGngCTPn9uHLKvnT/q4kx4vrF5agb4TtK0B7prM/QM14qke6tCnU7cKKzT2L5PGYJVgdV5bvhB0YtY/1/fhECH/z6/cj+uzMwCizLG3djTKost7rUa1Bbs1lp0w4tHIVkummVFbWDgEBgZkPHjMSkhnmzwAsBNAy/fetAD5yo1IzGfF21jEL1kandsAanwqhw38BzxzsUBwTZRmYlZ2mmI8MjI5jfkkO/nxuEQA4YhvJIka7mxujyJcb/c4mAeEyl1WZs7FPBNixbdX+Tpu8Ru+ZUEjGR93DiraP2OXWFGcZki6zGk09EqxHulg29zSS56ZZgltlGx1Iq3yZePjmJRF22M2ratB+LhDVZ8+1nsEDaxfiwVc+irLnptX9+gXFTJLJqtfC0mxcd5XP1bnslAkHTa5o7yvO8SbEjaaAgEBsYSaD5EEAK2RZnpz+OwXAflmWG1ysn2UkegbJRAHLLrksz4uCzDQlegQtI+GrGxshSYiyTzZywuIl4qwsljubljM1nU44Qek5jJKoK6nJEh68cQlSkiTbzlaxyizJWxc3iB4rq6U646QT7zYqwyi7Jm8GSqezdEY6hibh9h36WWCtwqh9p8+N4PnDXQjJUEzEbqsvx7+/ER1hY+vXl2JiMgTJA8wvzsHsQvZaYVR3M5k/rbabts5dkeeFb3qdszN/j3adx97jZ5W48wAcCX8pICAwc2Arg6QK+QhHIOmf/jtr+jMBG7BKtJwiaDwaYZbmiWQKVG+i7X0jzGt6s3FmrVzru0HWtqyrhS8zBW+fHlDCG95eXxFle2nlQJEIiVvUcNq2lfTFx73DVI1y30hQIdtO3E4Y1d9Iu8tr9mHXLEEtI6W5XnzUfTFKS/PqOa6ZPOi1LzRt31Ply4zQbi8pz43qs02ra/B3v/kQA6PjEWuB1X5x24xKLVu9Q8GIMKGVvnTugzPNROb3x3qjHMxbWjtRU5yFVzc2RplbCQgIXH4wQ7b/CcBhSZJeByABWAng79yo1KUCnpi2dpOMOEHQeOyJeUmQ3mYLmEveYtYO2wmiyHKKfOruZUpEhe98YQ4zS5z2QGG0kZ86R6/zPJXGN5Zwi0Te21hNPThJCGubSb/YJV1G9XfK3MiOWYI2Zv3/vGEBPj47jHsbq7Hr0BnLCa/sQFunppXVmFucjQUlYY11KCSjpakBPYNj+LB7EE8f6FBCfarnmNvmGnagVgwQ7Xtprlf34KwGbd3dsb6e6mDe0tSAJWV58HikuMxjAQGBxAI32ZZl+UlJkn4LYPn0R5tlWe5xp1ozHzyEmEUO502bZ7BIejySI/CSILvh+bTvNEOMnCKKtDJGxyeVdulliQMuHih4NvKOfvr7OvsDcdmknSKRRENLbLR3HYqOWLNpdQ2+2/Keoh11QpvPU38ntKh2HHLJ/M3PSMX6hkrF+ZBoRfd80G06uo/dmy5tHH2SCXZ3cyMAKNrbexursX0fO4zfTHAQV68TRunV1aCtu6zMsBcmpoQWW0BAQAFXUhtZlo9LkkSyR346/f8rJEm6Qpbld92r3swFDyFmkcNjPUP4m1+9zyRoiZxZj7XZeiTAI0mmiZwZh74LE1PYtHoOnms9o2jdzBLFEz1D1DpW+TKxY309Wjv6UVOUzYyXy7ORlzU1YHR8CsU5XuR6U6jvgxQ2R6Fp18yQKrPPO0EiSVvUERlIxJp7VlSjtixXVztqB3r15+kL3v6yoyEnMkKTDxJ2s6W1kzvkoRM3XWZupPTmsJsO4mayX+qNnfpApndw1soirY/icQshICAw88Cj2f4egCYA/0L5TgawytEaXSLgIcQsLZzavpVG0mN9VcuTeU69yZHIA71DQWSkJKF3eAwvHO7C68fPUuMv29F6sbLPPX2gQ9GYqss3SmiSn5EapVXcfudSHNNkPnzwxsXY/npbRKZNbbxv1ka+9/hZRXP4z+tq8Q83L8b/fOFi4pbmVTX4u998gCfvWhax4ZslVVZImBMkUg21nHYPBvHEm+346TfqdJOc2AGr/qGQjFc/6I5IHU7LXmmmv6xqyMn8ZclHkiecBImYIRiBdbD33b0Ms7L5nP94b6RoNxTaOeaG/bXZ7Jd6a5Q6Xj9pJ89aSuujl9/vwj/evAR/q8mkmUiafAEBgfiDJ6lN0/T/v+B+dS4d8BBiVmrfH/7uRERZWiISq6vaUEhGZ38AZ4fGcLxnSLEpVWeWZDk9Xr+gGMd7hnH3c+9EkMjfHu12NB43K/vcUxSiwZPQRK2BlSSgcU4hCrPS8JVHI1PBP/DSB1TNo3psAPpGPjX9Z3AihO/vOoLHv1kfEVKRZO8kY06Iwml/ACd6hrjTWKv7pjTXi1vqynG8ZwhleelYUparS7iJLPUOhTWbVggbK7lNZUEmN8GxAi3ZC4Vk/KndrxBtgH6IjZV5FpER1i3K6vlF3EQbYB/s9588h8f3t3NpuY3WFFJPt2LqG8HoQOGR2P4grDVqz6ZGnBsZQ01RVtQhjLaW0vpo46oa/Odbp6mZYQUEBAQIuG22JUl6E8D/AbAfwP+VZXnYtVpdAuAhxDQtnEcyTn0ei1jeoZCMfSd60dY7EkGW1Knce4eCGA5OUIl4S1NDFNELTk7h2yuvwsO7j+G6q3y2olv4A2NITfKgezAY8W4gvNHKkLlsLrUJTfIzUnFLXTkkCUiaTo1NiK0axC5TL953fyB6Iyf9py5nYiqEJ96MDq1GEoRoiYJ6DPQ0woSEleZ6sb6hUtFG7nhDn4BZNUvQyvzA6Dg1IgOAmNr1nvYHmLa16r6LlXmWxyPh+gXFqChIx5UFGVHp6HPTU/DWKT+37bVerHzWgYF2w8NaU2jjSmLqx4pUGh0oHr55iXIIVX+v55i9u7kR9VU+1IVkLCnLNVxLtetueko42df4ZDiKS0gGDnX0o6IgHVWFwilSQEDgIsxEI1kPoBHAOgA/lCRpDOE423/tSs1mOHgJMU0Lx0NEzFzVWnGeOu0P4MiZwYiUw2qb0ifebMfElKzEA9aSQEIEtUSPmHmU5PBrMbVxr7fuPYFV80siylS/m6UlNUposv3OpVGHi3klOZhXlGVKE6seG/VGTjZnNSHwpnhQUcA+mNEOCGQMfvL6Sd16EBJmxgkMsK7h1ZN5rbOn0dxwMvZ471CQy7Y2VuZZoZCsOByqI3/ML87G6f4A1mzdb+uQoz3UaQ8Meocp1o1GvBN1GR0o7nvhKJpWVkeYJ/E6ZvP6hXT2B9A7NIbA+CQqCzLRPzqG8Uk5an2r9GWiokCE+RMQELgIM9FITkmSFAQwPv3fFwAscKtilwKs2C46vbGRjXXLnmNYW1uGJA/w+coCXFvtQ3Kyh/k7QlBYNqVb1tXigZeOMol4aW46k+ht3duG6xeWmKq/lhiwCOgTb7YztaR6ZMrjkTDbl4UNzx6OIplP3vV5Q1tzFjlUy0AoJGPzmgVRbaksyECSB3jq7mUYHZ9ERUGmYdxiSTJOY01I2PGeIVMaWzsaXl6ZN4r37GRoy+IcL15+vyvK1njLutqIvmOZdXkkUJ1VrYIV+aOlqYEqf2YOOR3+AA5/eh57PuiOuKFRH25Zh6mFmxoj4n2r+51AlgGPFA5ZeXY4dkmYaGPz11+ci1/86bTShrnF2cr8JuNbkZ8BgH3Q4nWapd3y/fO6Wtx9XSUe+UPk+nbfC0dxzZV5IkukgICAAjNmJJ8AOAfgWQBPANgoy3JI/1cCVuCkg9FpfwBb9hzD7fUVUUTjq7VXMDfJ4hwvkiT6JrV6fpGSwl0NQsQfue0aLCrN0SV66mQmRvXXEgNWmbVlOdg9baNpJlIKIVxnh+kk80C7H79qPaPYVXskYGFptmnHOtpBipYUgyQJIeNAG4PGOYW4ZWmZLtEh7yvLS4+4oSBlGGnE3dbwsuC07XSVLxOb1yzAlj3HdG1rSX/N29iIYz1D+Lh3GD/83QlHQxMC7MMMy1yJdsihkUQSem9KlpGekhR1Q0O0raz39w6NMUORnugdVjTxscqMqI3ZnposoWllNULThN+rUhZ4UzyYX5ytRAyaCgGPvHYCKUkeXL+gmDrvWanltW1h3fJ9f9cR7Fj/uZiYHgkICMxsmDEj2QZgBYA7ACwF8H8kSXpDluVPXKnZDIbdK3Cnr9DX1pZFaZc37zqCJWW5zA2hypeJJeW52LS6Bjvf6YzQii8qzUXnwKihc5cVokerv3YzY5kE1BRnWzZzANgkcyoUjqTxk9cvXlFfW+1T7DLNkEPtQUov46Ze3OLPVxVwyYTHI2FJWXQGQB6NeLxiJTttO62Me0k2l12uJEEJvUngpKMkS87IbZDRXDE63LFuaIwSzwTGJ6n93tl/Ub5vqStXiDatbKegF7NdXWf1bVZykqTEtCcgttm0ec+ae9qEUnq3fFMhOa4HUwEBgZkBM2YkWwFslSQpC8DdCGePLAeQ5E7VZibsXoG7cYWe5KFvFHrkxeORsGpeMWqKwpvzfZrQVixtkTqKghWiR6s/LdzWA2sX4sFXPjJdpt6tAY1k/uPNtfjXvZHRYbwpHmSkXhR7O+QwFhkPzZZh9DwtZXXnwKgjh0PAHc26mdsi9ZgQ515JAvpGxhwxmWCZq+RlJHPNFaPDHeuGxijxDCtKTEZqsvKZmbjUdqAXs139XvVt1lun/Lp10/rGtJ0dYR4u1GRb75ZPz99CQEBAgMCMGcm/IKzZzgLwJwD/C+HIJAIq2L0Cd+MK/fOVBZbIi8cjYSoEhWir68PSFtEcQO2QRRox+N6X5qGyIB0tquQwFfkZtm8DaHUdvDAeZYLTvKoGE1MX+1KPHBrdUsQq4yGrDB5bc+3z2sPgQzctxqP7ImOO2zErMKtZd/ImCLg4JiTDIxl73jB6RtAzV9l+59Ko6C3adxkd0IxkijUnAWD7nUtx5MwgQnLY1ntJeS6Kc9IiyouFJtcoZjv5W32bZeaQdtofwJRM10pnpCZHxuXOSsPCK3Ki/Db+5dawudfswsy4Oo8KCAgkPiRZlvkelKSvIRx9pJfx/SJZlj90snJ2UF9fL7e2tsb8vQc+OYc7Hnsr6vOdTcujwsS58XsaJidDhgk9rNRnWZXPUZLDAtn49CJX0G4DFpZmo3vQXt3a+0bw/V+/h29eV40LY5PISEvGU39qxz9/7RrD6A7XLyim2mPbSaTiJKy8u71vBDds2x9FUEh0FPL3bpOHQ5a23IjAEOc1LUFcNa/YtlPx8Z4hqumC2baxwOpLo/JZvyOHz9Jcb4SjY6UvHQ/euAQpSZLuXOCRYydstnkOR6fPjeD5w10ITW9Pb5w4iy8vKdV9rxl5PvDJObx/5jxkGRFlblpdgxuWlODDz4Y1B/y58CZ7kJ+ZhtGxSfSPjuPLi0tEiD8BAQEFkiQdkmW5nvadGTOSXxs88gyAOvUHkiT9HMBaAGdlWV48/dkPAXwV4YgmnwC4W5bl89Pf/S2AewBMAWiWZfl305+vAbAVYZOVx2VZ/ifeescadq/Ai7Lpv5+VZV1zlJzswVdrr+CKJasFqz3pyUnYd6JXsQ11kyQaaXZZtwEkFJidulXkZ+Dryyrx/V+/H6HJTfKEN2y9GMU8txTxDKtm5RZFLzqK+m8zZgV6JIlVBiFsA4FxtPWOKKSYEKY5s7IsEyEyJlq5t9I2PVg1P6Jp/h+6aTGadx5WbheIhnxgdAxd54OKLTMxWamryIsKT8eSB+0tVkmOF9cvLNHVvrPAQ4hDIRkfdQ9HjenCK7J1tf5m5lJxjhe/fLsT/+W62RFOl7MLMzE5FR2X+5HXPsY9K6rxv37zkVLG0oo8QbYFBAS4wI79Zh601fYXANZoPnsNwGJZlmsBfAzgbwFAkqSFAL4OYNH0b34qSVKSJElJAH4C4MsAFgK4Y/rZhATZCMMOT140r56DH33tashyeBMxQpIH2LS6Bt6U8NCQjSbJYKRCIRntfSM48Mk5tPeNRL2LENZlVT4AwFun/NTn9NpD6tO8qgbNLYfR1juC/IxUABc35tP+gGEbnQaLtJCmWakb6c83Tvbh0/7RiHbe/+IH2PVuF+547C3csG0/9nzYAwConpWFhupCxdZaj0ypQcZG/dtYgLd+apDDlxrelHC8Y4JKXzrSU5KYsqgFi+SxxosQthu27cdng8Eoh72te9vQOzSm+04jkGQutLY6ZTLB6suSHK/hXF6zqAS7mxuxs2k5WpoaFDMeINwHG549DEkCCjLTojJn3vfCUTx/uAt7PuyJKFtPHtQyWlWYhauKIuXVaP0h4BlrVlbY8rxMw/cC0fOQBhKh5ud/OoWpUJho11cW4Ivzi5k27+oDpXCCFBAQMAMz0UiMELW6yrL8hiRJVZrPfq/68yCAr03/+0YAO2VZHgNwSpKkkwCWTX93UpbldgCQJGnn9LMfIQFBNsKFmxrxbuf5KMdCI+1q92AQTx/oiEjf/fSBDl0tCu/1qVmzAaI9nJWdiv+4ZzneaOvDVOhiOvGte9sizAfccJTiAes2QE0AzdSNJ2ujlsjTtMHxCqHHa8NspX40reqDNy7GQGBMCc+2cVVNRLIjI7k3q+FVk7HRMXoEjdHxSd0+4oHbUVlo5W+/cykz1rVWi0tuew58co4ahvPscFBJ+qL9LtnjiZJbohwgsr3r0BkMjI4b3qqZWVd4xpr1jDZcqB0TLL0INax5QYoUTpACAgJm4STZtoL/AqBl+t9lCJNvgjPTnwHAp5rPl9MKkySpCUATAFRUVDhaUTPweCSEZLpjoZGjY3GOFwOj4xFh5owIEK85gBmzAVroLXV2NvL7ivx0bFg1B0A4SkistT2hkIxT/pEo56XvfWkunvy/p5XnzJBcnqyNPEQ+HiH0zBAQK/WjOfc98trHGBgdx8M3L8Hc4ix87WcHTMm9WdKvJmPnAmPMKBF24baJD618WQa+8uh+R/uP9t2coizkZ6Qqcssy3chMTcIp/4iSZIkGM+sKz1jzyoNdZ3KWeRprXiwszcZ1V/mEE6SAgIBpOGlGMm7mYUmS/ieASQD/6VQFZFneIctyvSzL9bNmzXKqWEuwckUP0M02jAgQ77vM1IkWeot25d01eAHb953E4/vbsXFVjZKxLVY47Q9gw7OHlduADavmoGllNZaU5WJgdFyppxmSa5S1cdPqGjz/7hnlOxYx1F73E9tXNzdpMyYZVuunjkW9be9JRdt/3wtHERibovZdhz/ANC0wK/Nq84v/ONiJv/7i3KjfkqRAdkHMSUja79M67dBCa+IwORmKMrXQmhDphe1jQa//qnyZ2LKuNsoMbMueY7i1vlyRW5bpxlBwEhuePaxrgsVOkhNdZ1pdt9+5FLIMpV8q8jO45IH1Xj1Z4wFrXlQVxt7US0BA4NKAmdB/98iy/ITq7yQA98uy/PcAIMtyg4my7kLYcXK1fDEcSheAK1WPlU9/Bp3PExZWTQisaNN432WmTtqNbNehM1HprjetrsHTBzoAhDe5R/e1oaYoSwnHZ0X7YzaMG6knLenMbosaSb2sjTdfU4ZT/hFuIs8bto+33UbPmTXJIGSS/BYAV1+x3jM6Pkntu8OfnseFiRCVzJuVebXmsXswiGff7sDP/vJzSEv2OB4VR++mAABzLKyGSLSybuiF8jvtDyDbmxRhlkbMoeYWZ0eMPW08g5MhBCdC6A+MKc9p25qRmswIoRedgkFb15KccOQUos1XR0Axmr+svtKTNfW46s0jJ8JtCggICBCYMSNZLUnSOoSjhRQg7Pz4f8y+cDqyyPcB/Lksy6Oqr34D4FlJkh4BcAWAGgBvI+x4WSNJ0myESfbXAdxp9r2xhh0TArMLPe+7zNRJvZGRxB7JHuA/7lmOiakQPJKE77aEyQ4Qtve8vb7ClK2uFlZsMFkbbnGO1/JmaZS1cXZhpmUib6fdPM+ZJWtW7V5Z76El+SD27gOj48wrfq3ME60wjQx5PBKuX1AckZr7B7/5AJvXLMDy2T7HiDbAvilYuKlR17a6sz+A4z1DuLexGkD4sHr/ix8opkgskwer6wat/8i4blw1B0+8GR3CcH5xtqHcyHLY4bXrfBB/+cTb1LaOT01FHcS1sehZdWVlcSThD81GZeGRtXiG2xQQELg8wR1nGwAkSbod4cggAQB3yrL8fw2e/yWAvwBQCKAXwA8Qjj6SBsA//dhBWZb/avr5/4mwHfckgO/Ksvzb6c9vAPCvCIf++7ksy/9gVNd4xdlWwyg+tNvvAqI1b+QznvjFez7swZY9x6KSuhD7xTVbL9qWfucL9A3dTExinrjDtHjMvz/Wiy17jkWklL+22ofkZOtWUrEcO954y7z9Y4ZIGMVsZmmKjTS+75zux/6T5yDLwPPvnlEOZTzx4nnaYDVGtVmw4sy3NDXgW0++TX1/lS8TL77XFeEcTUjgHcsqMDZ5MbLFX8wtRP10hCB1++3Knrp/Nq+ZByA6nrQ6TjTLKbiltRMP3rgkKg26uq/b+0Zw9y/extraMkV7/sqRLjx51zLDsbCbVyAUkk3LWqxkR0BA4PKCI3G2JUmqAbAJwC4ACwCslyTpsEY7HQFZlu+gfPwE5TPy/D8AiCLSsizvBrCbt66JAievIs1ce4ZCMk6dC+BY9xDazg7judZwVAF17GKjOpHr3rI8r6KtBi5qnl7d2BihVUryAPkZqUpqayCszTMTncTIBIJFwr44rwgTUyFLSXv02q/uTzeT9/CafvA8Z9Ykg1Xm3uNnlRjltLjMRu+ZlZ2Gx/dHH76MzKhCIRlHu84rWuFdh8LkSasFtptS3W7ElsA4PQoKsa3WOkdv29eGppXVmFOUFSG/NUVZqJu23SZwYt3wB8YU05Hy/Aw89sYnuhGO1OPZOxRERmoSJqZCWLO4xFDuSBg9K7d4diP2eDySaVmzGt9cQEBAwCrMmJG8DOA7sizvlSRJAvDXAN5BOC62gEXwbPpmtJV6YevMpn33eCSMjtMd3vpGghFkKystGekpSVHas5Ic/ugkNNOVJA+QnpKMyckQPuweVEjYGyfOonFuEY73DKEoOzqWMG9bjfo/FlfOTtvcmyFrrDKJBQBxfGxaWY35JTkR7dZ7T5Uvk5r6W4+AGYVcVJMhOynVnYjYUlmQyRwLFpmbMysLW/Yci5DTzbuOYElZLndISl67/s/OB5WbJrV/BdH6mpUbPbmzE7XFiYg9ZsuIV0hOAQGByxdm7tmXAbhakqTnAfwa4bjaNM21ACfUyTnUCVK0nvRmIkywwtbdUlceoXnjBSvxRlG2NyKSgjc5iZpcZHKK30yJbJqVvnSsb6jEE2+2Y9vek7h9xwG88F4Xmncexra94cgndyyrxCtHurBt70m80danq2Vkgaf/zSZcsQLeaBxWItVYeXfzqshoK8GJcFxxdbt5kpiMT8rY8UY7tu87iRff60JqUpJuMiU92dWSIVLvW+vLFaJNfmM0Pk5EbJldyB4LZrKaXC8zHrYReNcK0j7t4XPr3jbcWl8eVVfae7TjSpORLetq4Q+MMaOq8B5E9SLi8CbKMRtVx415JCAgIKAHM5rtxwEMA3h0+u87AVwL4DanK3W5gLbpb9lzDGV53gh7WZam7OPeYQCR0SOMwtaZ1d7wao06B0ap7/10YBRzirO53qVnuvLAS5HOZX//yofK3yQsoVlNFU+c3lhcOatjV3f2B5CRmozinDTmc3oaRLMmL9oy01OS0LzzsKIBBS46ygUnwuHcqnyZhpphdd8S51l1ynCaJpnV10keRMkcqbd23Mlv9MaHd0y1fbmsKtL5kjUWrDlTlJ1mWaNqJqY0q31Lr8zDzqblTM2znsZfbWIyMSXjgZeO6kZVMQOaRt3sjZKZ2xy346cLCAgIaGGGbC+WZVmdJv11SZISMovjTIF2U2RF9FhYmk3dpI92DeG7Le9FbEJ62c+saG94N6ZMZvgvc3mT9ExX1OmS1X/TwhLytJWHdMXyyvlEr7nMgWqEQjI6+wOWspZq7dO19rfElMOb4sHEVNgfwMwh5ZY6uvZ5vsaxc3JKpvb16vlFWFKWRw0ZSFKqa39DUp7TDh08Y8pD9lhjoReKz6rJhJlDHyujaqUvU5eMGhF68lu1c6EZky0zsJuwxggitJ+AgEAsYcaM5F1JkpRY2pIkLQcQ33AfMxza62YWKZkKgXnVr70Cp12RPnzzEtyytMyy9kl7RQwg6nq3OCcNm1bXRLx30+oaqobWbL+Q8tSBc9R/dw8G0dLaiZamBlPJWfRMZAicuHLmuQ63Y65CiOHzh7uoWUvNmLwQovjKhhX46Tfq8KOvXQ2PB0hNltC8qias0ewPGJrtqPtWkmD4/Gl/APe/dBTNqyJlaMu6WirRJmAlSfmoe5hpcsEzpnbNh9RzpsqXic7+AN453Q9vsgfP/b/X4td/1UCVU5as8Mgq+T3JqGpWZnmSXllN1mUWsXqPgICAQCxgRu34OQB/kiSpc/rvCgAnJEk6CkCWZbnW8dpd4tBeNyd56KRE7Yz4ce8wjnYNKU5j5Bmi4SIxiFuaGtA9GERprheLSnNthcFTg6Xxu35BMWqKs9C0shohGfBIQE1xlqW02bRr+B+sXYSfvRFOWuNN8eAfbl6CbXs/Vv7evGaBLinjfQ/LXMHIdOPUuQA6+gPInDYBIZE7eK/D7ZirEGJ4b2O1YyYvH58diajzA2sXouXtTnT4LyAzjX6LQTukfO+595Tv9Z7vHQqiw38BzxzsiIiaUZbnNWUGw5PynGdMnTIfCoVk7DvRi7bekQjn4UduuwZ1FQXczri85lwko2p+RqrSjx4JWFiazX341BunWN30CCdGAQGBSwlmyPYa12pxmSLaXjYZO96gh7AimjIA+G7Le8xNKBSS8ftjvaajZ/Da+rI0frubG7FqXjGqC7Ns20Fqw5BNTMnYtveEEke7vrIADVUFWHplnq138ZrIsGxKT/sD8AfG8Nn5YETYwU2ra1BTnIVV84q5r8PtkAs1MXSCoNDq/OArH+GeFdX4+OwIirPTdIkf6ZtZ2aloaWrA+FQINUVZUaEZ1USRtF+dCdSb4sG6ujLD+mrH58An5wyJspEZgRNkj4QxPHJmMGJes2TASFZ4Dn19w2MRiXTIgfy6q3xKmD8WeAi9E9FDjPqMrEOPra/H/RrbcOHEKCAgMBPBTbZlWe5wsyKXK7T2snY3Oyu2jmackVgaP710zk70S3l+ehTJcMLm0ko56v66Z0V1RDKf4EQ48kPTympUF2Zxa0jtkBhCDK3armth5KhYUZCJioJMKvFjydJXFpdiSVkukyg6SeJYRFmChPa+ES7ZtFsf0g/He4YQktlmNNo46nqx6vVkVS9s4sDoONJTknDgk3O6c5Pn8OmmcyGtDVvW1aIsz4uCzDThxCggIDBjYSqD5ExCImSQtAKe7HF6z1jJyGYmoxrt2UpfOr73pXlRmsuFpdnoHmTHr3YzUYybUPfBhlVzsH3fyahnNqyagxVzfCjK9nL3rdXMgWqSkp+RilvryzG3OBsLSnIwu9Be9kF1nVuaGgxNdexk53MicyIrodOm1TX47dFu/MX8IswtysaCUuO+sVOfT86O4CuP7se9jdVIkoB/p9xYafvk9LkR/PaDHt1Mjyyw+r1pZTUqCjLw6L42x6KHuAWR2VFAQGAmw5EMkgKxAY+mVe8ZK9ffZuxTaRo/bTpnok1vWlmtZCFUb/CxSBTjJrT9xYr+Qggar4bUqrbeaW0jq85aok07MNmxdbZ7W0GTq4duWoL+kSBeOdKNNYtLo7T+ejJnpz7EiXTXoTP4q5XV2LS6JspmWysDUyFQY9Vfv7DE8H2sfl9SlouHXv1Iie/tVvQQFniTdp32B/Bx77BjPgcCAgICiQRBtuMMpzW8Vq6/zRB0GrFjbfQk6IZ2g3c7rJfbUPcXzXSD2GyTsYxFTF8tMSRRLazIFa9TKO3ANK+YHqYyFo5tNLm6/8WjuGdFNRrnFhmGHyTtIvOxNNeLqRBwdth8H5JQmN2DQfzsjXZ889pK/OhrVyM1yYOrirKoWvWzw/R51DcSxFVF+vOCNYdzvCnMRDpOzTXWGsZzqFY/c29jtXCKFBAQuCQhyHYc4YaGV02U+gNjSEnyYHR8Cqf9ASZZMEvQaRo/2iaptlBSb/CxSBTjJtT9RcIO7lhfj6lQSElIQ6KRABf7q8qXidP+AN465XfVdMYJuTLS6rIOTHs2NbrqQKcHPVtzHrtprTnON6+tjNJG8/YhCYW5dW8bugeD2Lq3zdAkxI5TJmsOF+dYT6TDAz1Z4zlUq59xyudAQEBAINEgyHYc4ZaGlyT7ON5jnCSFPM9KwsHSjmo1gNqNftPqGjx94KJPrXqDn+lhvaxoq2NpOuP2zQEZexp57RkKxkSTT6tTBiOxUn1lAQ519BvKnLrfbqkrjzLpMNOHFQWZpkNh2nHKdCORDg/0ZI0n8636me7BoBL6sbYsBzXF2TPKl0NAQECABUG24wj1RlOa61WiEPSNjNneZFhZ/uZtbKReSWs1mZOTIfyp3Y8jZ86j0peJo2fOY0FpLq6tDqes1hLH7XcuxasbG9E3EsSsLC/OnA/g1vpyhGQgSQKWlOcqG7zb4cNiAbPa6liazrh5c0AODSd6hpjk1ShqhtOOsaROW/Yco2pGr6v2oaIgHZW+zKjsmmqZU/ebXiIe3naZDYVp5dCr/T2t3908/OjJGutQrc58q82O2z0YxBNvtgunSAEBgUsKgmzHEWQzys9IxfqGSoUkPL6/3ZLWU73hj4xNUjfBzv6Aof3n5GQILx/5DH+rIibNq2pw/0tH8b0vzcOi0pwo4rjh2cPY3dyIhupChEIyjvcMK7GFvSke/Mut1yjlx8qO2W04ETLRDdMZN28OyKEhPyPV9JW/W9p99UGGaEaTPIhI815VGNYqX6MTm13bb7x9qNcusw6W2pCXtMgqTpsE2YGerNEO1SQcITlsvroxfmZHAgICArGCM2kFBSyBbEa31tPTtJtJs002fJKiemwyvHGp4U3xICNV/3wVCsn4U7tfIdqkPtv2tWFtbRkeee0EPuoZYhJHIKxV/2+/iiTj/+1X7+HUuYvt0aaAn0lEmzgf/vHjszjRM4T8jFQA+uPGm26b9S69VO9aOJFingVyaFBf+W9YNQdP3b3MkADaTYFuVCcASlKcbXtP4sLEVFSMaD2ZU/fbrkNnuFOeu9EuMp+/8uh+bPjlYfz7G+1Y31CJ/IxUR/qM9U4nZY0cqnc3N+Jnf1mHe1ZUR2W+Jdlxdzc3YmfTcmoKewEBAYGZDqHZjiPIZqTVDAHmtZ7aDf/MwGhUuLFNq2tQnJNmWE5rRz+1PpIErK0twydnR3S1fiTsmfb3PFr1RIde8pDuwSBz3KyYzuhpTAEwzTHcvDlQazIJsfWmePClBUWGv3VLu++UJt/jkXD9gmK0NDWgezCIK/LSsWZRCXoN+tAfGFNSowMXMzfaaReNwG/b14Z7VlTjJ6+fRIeOw7MVWL11MJI1nsy3bmreBQQEBBIBgmzHGcSZ0S5Z0BKZp/7Ugb9aWW3KQYuUE5LZ0UWSPMBzrdFRAx6+eYlCHDMZjmpGWvWZACMSZCZkohFZYmlMF25qxEfdkc6vW9bV4iuLS5Gc7FHe5ySBCYVkdPYH0Dc8hh/fdg06/AE8dSCcnfCvvzgXb7X78Wn/BWqyGGLedGFiCptWz8FzrRfTiKv7y6o9t1M+AKGQjN8f62USTlo4RQD47HxQySJKDl8trZ22zHZYBxNJCvfZ4U/P48JEyDEtsNo8iPiOnOgZwsLSbMOEOjyydin4aQgICAhYxcxnPwkOHgJB24i2rKtFRX4G93u02r3uwSB+/qdT2Pb1pbgwMcWt3SzO8eLl97uiyPQDaxdixxuf4KEbl2DHG+2KCYEkhYl8XcXFhCfqsGdmtOrxgFmCZ0SCzIZM1APrXb1DY1EkfPOuI8jPSMWKOYWOX8GHQjL2nehFW+9IxJj+4KuLEByfhEeS8I97jjMJqlZjSiLVEPvjKl+mLXtupzT5ek6sVb5Mav0WlmYrmVPJb7bta8OO9fW2iCRLW++REJGGneVga0Wutb4j3hQPKn2ZKM/LUA5xVnGp+GkICAgIWIEg2y6Cl0CQ6+sd6+vR2tGPqRDwyGsnkJLk4dZc0Qj75jULDNNr08rZvGYBtuw5pjia1VXkIy89GU/etQwV+RnKe4gm95HbrgEQThVfmuuFLAMVBRmmteravjvtD8AfGEPqdKxwu9ErtASkIj9DV5NJA4sENc4pxC1Ly7hCAPKSINa7AuN059fWjn6U56c7fh1/2h/AkTODisMred/fv/wh/vlrV+P7v36fGWWlsz+A4z1DuLexGkDYxGLr3jY8dfcyzMpOU9rf3jdiK1qLE5p8PTMXANT6PXX3MupvUpIkx5NTPbB2IYYuTODpAxftnmmmKlYOLsU5XqrvyH0vHEVhVpojhzgnEy8JCAgIzCQIsu0izIR76xwYjUh5DsA02XBCc6SUU5LNLEf9nllZXpzyj2DN1v0RiUDyM1Jxa3055pdkY0FJtGmBHtSh3G6vr4iKeLGwNBvdg+Y2aBoB2bKuFo+8dsIUwWNdh3++qsCwHrwkSH3Q2LKuVtGckucrC+hmR1MhOG7LC1w0LSLvU4epzEpNQn5GqkL+gIsEtcqXiXc7z0dEpSFaWRlyRB8nQqIjPdtvVv1GxyepvynOsRf5RTuf01OS0LzzcEQ2SG+KByU53ijCaiXMZJUvE3OLsmN2iItl3HkBAQGBeEOQbRfBSyBCIRl9w2MR2j89ZzsWnLLTNYohrX5Pe98INjx7GMGJyEQg3YNBbNsb1nzvbm40tYESsnDPimpqlJamldVK2bwbNI2AbN51RLG1JjDqczuHGh4SpCUhlb507Fhfj5QkKcJOWEvCiZ1wkgeO2PKqNfAZqclImjaToZkaELMQrR32aX9AiWtN2rttXxuaVlZH2TMnQqIjI7tiWv0qCtyzRdaGAdy8ZkFUbHut7f4jt12D/IwU0wcXj0fCgtIc5iHO7KHH6AYnlnHnBQQEBOINQbZdBA+B0ItuMTA6HresiryaJ55EIGa1raRMVnkkIpmZDVovlbcaPATP6qGG5/ClJSEd/gtoeqY1KsnHVxaXIj8jVTE7amntxO31FYa2vDygEf6/uX4eNq2uwYWJqagD0Na9bVEHoCpfJt465ae2d+50ZkA1EsGBTu8gxarf7MJMzC7MpP7GyQQ+2kgppbleZKYmY+32N6MIa0vTtZYOLrMLM5mHuHV1Zdx15Vk7EuEmQ0BAQCBWEGTbRfAQCFZ0i6aV1ZhfkhM3b31ezRNPIhCzkRPUMalZUVEIeDdo1sFnfslFbZ42qorT4Dl88ZKQ5GQPVswpRFqyB/tPnsPa2rKIGMZOhp3r8F/Aj35/Aj+5sw5nBi5Q67f0yjzsbFoeQTZZ7V1QkhMlB4niQMc6SBnVT/sbp80kaJFSfvS1q6ljMTE1ZbjusA4CtEPc5jULTM0JnrUjEW4yBAQEBGIFQbZdBA+BYJGrpVfmoXHOLMdTW/OCl/RV5Gcojp1pyR7c9+X5ePi3FyNTEC19arKEsjwvl6MjOaT8/M1P8IO1i/D3r3yolPeDry7CL9/qUJ71pniQnpKEA5+c0y2XdvDZtLoGP/vjSWZUFSvQ02byHL7MkBCPR8Ks7DQ8vr894vlKXzpXn7Dq7ZGkKDvsDv8FjIxNYm5xNrV+lb5M7tjiswvNR2txI827WZi50XDaTIJWXtvZYepYFGSmoa6igLnu6B0EyCGuPD8dZ4eDWFdn7PSrBc/akQg3GQICAgKxgiDbLsNog2aRq4qCTNORMpwErwmMto7/vK4Wz967HPtO9EGWgWcOhonx7fUVuH3HQa62kENKWZ4XzTsPK2RYloGf/Z+TuPGaMhzpGoI3xYOHblqsOI4ZORzOyk5FS1MDRsfDoRBP+Uewde+IUtYjt11jKmKKFkbaTJ7Dl1kSon2+0peOjatquPuaVW+WHbaZ+jmlrWb16/ULitE5MBoTAu5UiEirNw608p5rPYOHb16i2MWrx0Jv3TE6CNj1/eBZOxLlJkNAQEAgFpBk2Tgl70xEfX293NraGu9qGIJFJBaWZmPN1v1RG5bWdjfW9VKTtva+EdywLbqOLU0NCtkDgO98YY6S9MNMWw58cg53PPZW1OdP3lWP9NQkZoSG3ToOh2qi9se2szhyZlAJT1hbnotV84otb/is/jA7ZoTY8ZIQ9fPpKUkRfc9TB1a9WY6oZutnBZEOmvQ27Vhfr0TwcfMwasUkxIws8BB5Vnl7NjUiJEOJDJTkgWGkHta82tm0HA3Vhab6hgYRaURAQOByhCRJh2RZrqd9JzTbcQZLw0Ocy9Rh1gCgPzCmRCewc61u9Hs7JjCj45E2o0keuqOjkZaPpSEjJgsHPjkXQbRJub1DbIfDi05kDUoUFXXZeqTUqM+c0maa1Syqnz/wyTnTddAzZdLaYavh1jldS9aaV8+h1q+1o98xMw09WA2lx3MDwEtMaTcYD964RCHW9RUF3DdhbttLC621gICAQCQE2Y4zWASuOMeLSl96VJzpmqIsXDMZsmViwoo5rU73DVg3gSnO8WL5bJ8qRnByREIU8tysrMgYwRX5GRFmAeoEOjTCwnr/xJSMUEiGxyMpmfHUBxZ1aEU19EgpDymyS2KcsEu2UgejQ422jm5rLbXkNiTTHWWnIofPtWgWVg5RvISTl8iry+sPjKHrfDBCq28mZrxde2keOXUqDKmAgIDApQBhRhJH6BEXAHjz5LmoRDc0Mw3yOa+5AutKesf6elOZ4oyIl3pTnpyScf9LRyNsq1OTJUW7TGyN73/xA6pd7tnhIEpyvErM3+IcL8pz0/GnU3682zmAkAy8/H4Xbq+vQEtrJ568axmqZ2Xh9LkR/PaDnqjU8Y01hfjazw5w96GeWQCJR85qJw8RNUNi9ciOFTJs5jdOmcrQ6kDadGFiCv/lFxfnbmmuNyq2NyGXeiZETsGtNgPWTDpY9dHGjNcrx6opkDARERAQEKBDmJEkKIy0WilJElWjZlYrqwVLU0cyxanJI03jrE5uw9LesbTnZXleFGSmwSNByTp5S1040+R/p6T9JoSmypcZVd5DNy3Go/vaFGL7wNqFaHm7Ex3+C0pfTIWgEG1S7ta9bfjSgmKqdq8iP4OaQprVZ/2BMRzvGY5qZ2VBOiRJwuj4FE5zxBk3kgV1VsnPzgejskqaccLUwsxv3IiPrJWVTavnRGiyuweD2He8B/9xz3KcGxlDaa4XC4pzkJLkcTyaBe0gY1YTbOaGwspNhN2Y8XZuUEQyGgEBAQHzEGQ7jjAiLqyNuCAj1Za5AqvcqRDgD4zhaNdgBJnTklotuVNfF4dCMtr7RtA3PEbN2EjI84FPzkVkI7y3sZraFx/3DsMjAYOjE1Hl3f/iB4o2LzgRwoOvfPT/tffu8VHVd/7/6zOTy2RynUzIxYRJCAkQIAHSiOgaupLWoqWriLfaRetq8+v3VwitvdC62nZXa4t12RXpbr+o6223FZSqVSnVgvtTt4ANKqDcAoHEpLmRhNwnlznn98fM53Aun3PmTDIhoO/n48GDZDLnnM/5nDPJ6/M+7/frjbuuLMTx9n5lLtr7xHN8ZmDYIDB9Hrdpeo7ZnAHMMK6Nbx7DPV+cbSqIRVjdC+qFxl1XFmqKTUViZzyP8O1uMxn5vnoBt622CeuqipVFUkV+Kr51VTHeruuAJAM//8MRrF9egqtLsrAjinnBVlFbu4uRSCO/IiH/0MpSOBiUVCg9ZtegIj9deX2iOeJmUDMagiCIyHGEfwsxWaibt3DUwoX/Iebv4SkQD75+GOuqijWvRxLVK/AGO8Xx7fO9Cdh480IkxDogSTDkft738kdYUZarfH/Ptg9xunNAs09JknGyvR87DrXglQ+b8cEn3aZ/lPm531SRp+lGKJqLE+3BNJBdx9qF+2NM+73TAWFet2iOucBcUpiBwmlJaOweVERITqoLd11ZiKOtvTjU3KPkj6vnvGZZMfbWG7skrijLVYS21Zyp0Y8zJ9WFmqoiDI4EcKj5rDIus66afF4nCl8s7Tl5BvUd/ZAkbZqZ6J6caERZL+Baevx4dk8DnrlzMV785hJ87bIC/J//fh+bdp3AE+/U45YKHzbsPILG7kHN9ZtoGoNZ1PZ054DhXjE7ltU+RPCnCq+vrcTm2xahemkhfvnHY1j+6DvY+XGrYf4B82twRaEXO2oq8Xz1ZdhRUykU0JGOT0+431kEQRCEEYpsTxJ2HtWaPZ52MCgNSb4wOxNbq5eg+ewQJAnY8vZJHGzuRUf/CKqXFmLR9DTkhx512xUb6k5xR1t6kOiKFbaL5x7LIlGrjmSJomX3r5iLfG+CIadWvZCYlZms/NHfvr8JNcuKNXm5NcuKwVgwDeTuykJhNE9dcuCKdaBqTiZKc881pokkBYCLPn2O8Ja367FhVRl86Qn4r7suw9t1HQhIQQ/xVZ/LM4xrPO4r6nF63HG4/fJ8JbKrd+OYLCcJO1HPiTpNiD4Xokht9+AIpiXHAwD+/sn3lJ953HHwjwXw/36+CB39w1F1ubAbtbX6bI+3mJIx4HsvHNBsu2HnEWEjKKtrEO7pxEQj09SMhiAIInJIbE8Cdh/V6h0GYh0ONJ0dwksfNGNbbRPiYpihaLBmWTE6+kfQ0uPHpl0n8Hz1ZeN6fMs7xaUmxGiKLf2jwXbx6mIrkahViztRtOyB1w5b/lF2OBhKcoKt0nnetsMBPHLjAsTFOHCgqUcRs/5RSSjGeXoLH9PGmxdqhLZ+jsOJQy76bijXRtx5CsxdVxbC6QA27TpXhCYaFz+vSAXx7Kxk/PvXypEUH6PJX1e7cYiOp1+gjVeARuKMMR6nCf3ngtvXxTgYHl9dYSgsVVtgAuJCyWgW59lt5GT12R5vmo1eBOekuiwbQY33Gkw0DYhs/QiCICKHxPYkEEkRkcPBUOBNNBTZ8aguF9p8P2ohPNGIpsMRLOCzKrYyE7XqSJZZtOxEe79l9H1GRiI237YIdW39GreQh1eVYV99B24oz8P0tASsqyrCttomPLe3QRG8VXMyMS8nFeU+T9g/+nyOHQxo6x3G6c4B5KcnYkaGuHvj0dZe05QVvQ1dS48fW2sbsbV6CYZGA2Bg+Nnrhw2C+KGVpZYFdXoBp366oBbY/HhbVlcg1smUTpi8AZLe0WYqux7qUX8uuJjU29fxAlq1BSafb9Ei6J5tH2L22krMzJz4+OxEbcN9tiOJ/Gob98RongSZnetECxF9Hje2rK5AbUOX4uDz3S/Ohs/jtr0PsvUjCIKIDBLbk4CZaGkwcaUQ/QHftLsOv7xxganoi9bjW7NI16zMZKxZVgQHA1ITYvHMnYvR2isWtWb7GJMkzMlOwednZSrnqRd+M7xJmuYy/lEJ//LmMdRUzcI/qtpQ8/bhT75br4lg2/mjL0kydh9rM4h6sxSJ3LQEoS+4LAO/e98YWV6/vEQZT31HP46396MjtDBgLNidstyXZip0za4/X1TpBb36GtR39Bvm755tH2LuukocbukL+3TFzr0QrXxc9efC7OmB3k5PLV7N8tWPtPYaFk7jweFguLokC1url6Clx4+c1ASUZCVr7tvOgWHDGDzuOHT0DSvvMSvcVIvrnFSX4fqoC5HH2whKjyTJaOwaQFvvMEYCAXT1j2L9784V7t6/Yi78o2P4n7p2pXtqNPzeCYIgiHNMqthmjP0ngBUA2mVZnh96LR3AVgAFAE4DuFmW5W7GGAPwKIBrAQwC+Losy++HtrkDwH2h3T4oy/IzkznuiWImWj745CyGRiWD4DHvxDgm3E9lUQZuWJQblT+Cokjcuqpi/GzHESVnm3sKm/n+mjkqlPvS4EsPLgbMHr2L3EJWlOUqQpvPxaO76vDMnYsxLTk+ovOWJBmHms/iYFOPRkBbpUiU5qYazuc7X5iFp/982lL46ueCP33YePNCZR5EhLNy0wt6O9u29RrdYKLV9XC8qD8XVoWeZs1cOvqH8cQ7xkXQ8bY+zM1JGXfnT/X79G40eieeDavKNBHonFQXbr88H3c89Z7h3tbneeu7Yurvx/te/gjbqpdgcDRg2ggqkoWPfpEpcrJ54LXDqF5aiKazfhRmiC02yUebIAhiYkx2ZPtpAJsBPKt67YcAdsmy/AvG2A9D368HcA2A4tC/ywD8B4DLQuL8JwAqAMgA9jPGfi/Lcvckj33ciEQLTwvoHhwxCB4zcZ7qisWD1883NHq5tCB9XH/4zETH3JxkVC8thCQH84YfUgltwF6Bl1UeZ31Hv6nwE527WVSvo384onPnAudoay8kObJIYVwMU+bEwYAElfODmfC1MxcizK5/1ZxMXDHTayvPXL/twMhYxJHRcL7pE412qj8XfJzq7p5OBmSnGMUkf4JR4E3EQytLca/qiQf/XF0x0zvuzp8c0RMGvb3k+u0HsWV1hZL+clNFnsHHXbSoEXXFFF2fxu4hXDs/BwAmvPA53TmgWWSaLXC44Ql3tCEfbYIgiOgyqWJbluW3GWMFupevA/C3oa+fAfA/CIrt6wA8KwdbWu5ljKUxxnJC731TluUuAGCMvQlgOYDfTubYx4NakMzOSsZ/33UZ3jreAVmGxt2Deyfz92Ymu7D5tkVKOgAXET/feQT3r5iL19dWoqN/YsVIVqKDF1sCwJplRegeHAEQjNrdUJ4HpwNIiI1RfH/NhJdZSodVLvDiAq9BVFyq8gvm2Ilg6q8D9/q+u7IQTmbPxYNHwtWpGfy9diPrkea0mkWUzQS9nW3z0xPHlRIiGvtEvZnV98vcnGS8vrYS3YPDmH9JCk6dGdCk9szOToEv3Tz3vtyXpiyC+Oeqe3DE9LwiqZ8wu0/1TjyxTqakiZjVPOgXNaJ9h7vHJ1qI2NbrN4h60TH5LjOTXeSjTRAEMQlMRc52lizLLaGvWwFkhb7OBfCJ6n1NodfMXjfAGKsGUA0APp8vikMOj1nHxFcPNBvs76YluYTi5cVvXo43DrcptnItPX6s+c0HlikcdrESHeroKC/G21rbiFsqfBr7O94+nT9q97jjcFNFHmZlJqMkJ8U0b9YqF1gUTfV53KYRzMsLxRFM0XXgjXK272/CN5cWahqliCKF6ki4SHDIkCdFcIzX4YGLWI87FlurL8doIKAUFwKRR0bNFlET6RpoJdRPnRnAt7dGtl9feiLmZKfYPq9IxKPZfap34slKcSkLkvqOfluLGvW+c1JdcMUEU1Q+6RrEttomdA+OGKL0Ey1EzEpxaRaZIiebdVXFSIxz4pJQ51g+/snK2ycIgvgsMqUFkrIsy4wxY9eG8e9vC4AtAFBRURG1/dpBJEj0j5y5MHA6xI9qn7lzscZWjv8sGlElu9FlnpP8ixvKcOfTfzGMcWv1EkVo27VhC5cLLBIVZhFM/2gAY2MSYmLE/Zj018EV60BLjx+/frset1+ej0duXID4WAcKM5IMiwO+rZmnd2aya9KKxyIVVmYittx3Ls0mEgFvtr+rS7JwunNg3NFOK6Fu1t1zIilLeiIp+hTdp+GceMLd2/x+6RwYxoZVZdj45jHNIpYXKfYOjeIPh1pwU0UeBkcCqO/on/C9VeBNRGleqrLI5J/tX//95zAWkJAQ54QrxglvUpzyNIF8tAmCIKLPVIjtNsZYjizLLaE0kfbQ680Apqvelxd6rRnn0k746/9zHsYZEWZiVv3ImQsDtXew+r1mBZHhokp2BGCk0WWz82npCb4eiTXZeCK3vvREzPAm4ke66PZPX/0Ym25dhAXTPcLt1ON++1i7YuUnycDzf2nE+uUlqJqTJTw239bMy9rncV8wxWNmIja3eomSfhKJgD91Rry/LasrcOCTs+OOdlot8iIRwqJ7PNpNWMyesljZS4bLc9f7iosWsQ+8dhhrrirCNaU5lm45duZEP7Zls7NQNC0J5T4PugZGcLpzAD/63SF0D45g480Lcfm8jHH70hMEQRD2mAqx/XsAdwD4Rej/V1Svr2GMPY9ggWRPSJD/EcBDjDGurq4G8KPzPOawmAkH9SPncO/1pUceVbKbTxtpdFmWxY+TL0kNjt2umwQn0sitw8GQ6o5R7PPUee+tPX4smC7ejs+txx2H5fNzDGk9V5eIhTYQzFnlkXC9p3dpbtqE0imiCRdZovnfdbQdzWf9ES0AJEnGkRZx6kxtQxdeqBUvPuxEO/mcigS1XSEcac64XoSaWfGJEN2n4e5bs3tbf780dA5hT714oV2cmYx1W40Wjmb3ViSNswoykiDJUBxTONwiUpJhu/6CIAiCiJzJtv77LYJR6QzGWBOCriK/ALCNMXYXgAYAN4fevgNB278TCFr/3QkAsix3McYeAPCX0Pv+mRdLXkhEEkEze++MjGCjlUiiSpF0/YskYuV0wJDjvK6qGMmuWGy8eSGOtfZOem6nN9GFJ981Fipmp5qnc6gb06gj7x53HE529OPtEx0oEDTYkSQZpzr7NY/c9Z7eF0LxGBdZZvMfkBDxAuB05wDq2vtM96defDAGVBZl2HKF0c+p6HOhd32JizHuM5JFjpUIPd/iUXS/6JsiAcHv09yxYe8tbRMcZ0QLv7Zev8b5BQg++Xm/8aymNoJs/giCIKLPZLuRfNXkR1WC98oAvmWyn/8E8J9RHFrUiUTMhntvJFGlSARgJBGrlh4/nt3ToIksP7unAYt8aVg+Lxtzc5KR7000/KGOZm7nvJwUg/Xhg9fPR0lWimVUb/m8bI2gMWvzPTcnGS09QbHuYMCa33wAjztO04xmbk6ycl1ETyTyvQlIiHWGbZUerVxvLjw97jhDtJkX2EW6AGjr9WObIHr90MpSPLrrOIDg/cB9w29YlGtr7Kc7ByznVN+QBzjn6R7OycPsHC+Upw+A+H559UAzNqwqw/rtBzX3YlZKvOXiVeTTLZqTtl7xded+4OpFzwPXzcdv9p2+IOaKIAji0wx1kIwikYhZq/dGIswmq+tfVooL3YMj+NVb5wo21TneBRlJ8KUnYuH0tEnL7YyJceD6BbkozkxCa48f2akuzMtJRWP3oKWg4hFuPi9m+eXVSwuxadcJRVh63HGKqORcMdOLgozgNdI/kcj3JmDtsmLcsmWvZWTQqviwsXvQ9DqL7gMuPHm0+Zc3LkBde5/GwSbS68+v9XO6rpefy0/D+uUl4y6WU49VNKd2RXQk9/iF8PSBI3qCtX55Ca4uyUJpbqrmcwNYu8eIfLpFcxLrdCgWnWrGArLBD/z+Vz7CxpsXovnsYcWWdKrmiiAI4tMMie0LjEjzUyfLPcDOficrt1MvMktz07BgeviOm2qRYKfNtzvOiW9dVQTGgIbOAdx5RT4e+sMx5T16Qad/IpEQ61SENt+nnYYm/lEJG3YewWhAMkQ4+XU2uw9mZyUrIqulx4+HdhwxRCw3rCqLyOLP53ELu15O9yRiuieytCY14USyXREdyT0+2S3nAfuLYasnWKLPjdXTLv09LyrirVlWjO++8CHWLy/R/L6QJBlHW/uEn4Gjrb24oTxPWQyRzR9BEET0YbJ8Xh3yzhsVFRVybW3tVA8jYuo7+nHtpnfCPlpXw//4TzTCLBJhjd2D58WVQH3ssYCM+145pLTI1i826jv6cefT72FFWa6Sf/rqgWY89fXFhmYspzsH0NE/jDv+U1sclu9NQPXSmXjgtcOax+qb36ozPa6ePSfP4KuP7zO8/nz1ZRpfdNH7vnVVkaZ1NqC9zmb3wc51lTjc0qcRnvdeMwe9/jGMBCRU5KfjikKvqTViuCi72bUeTxpMuIVjJAtLu/f4RBvwhDvfaO0/UkT3Q743Af983Xzsb+hGQAJ+936T8mRD/fviZHs/fn+gGf9X0P6dFwHzJzyUs00QBDE+GGP7ZVmuEP2MItsXGON5DB6NCDMXERt2HsGKslw4HcCl+elhm8joGRuT8HFLD1p6/MhJTcC8nBRT4ac/tqi9fUuP3xAt9nncWLus2JDL7fO4Nfvl8+LzuA253z9cXoLv6KLN97/yEbZWL8HQaGBCbdatGppwzFrS8+tsdh+09vo1EdBpSS44HUBr78QKark4m2jLczXhahMirXOwc49Hw7rO6nynKidcFN1fu6wYx1p6Lb35udPMttom3L9irmZxyZtXbbp1Ea6Y6SWbP4IgiEmCxPYFxvl4DC7idOcANuw8Ymi4sWFVGb5SdomtP8BjYxJePtBsEMHXL8i1FNwiAbNpdx3uurIQv3rrhGGx0dg9qByDv/++lz9Cuc8jFDyN3YN4LLQ/xgBXjANDo+I220OjAdvdOs3SGxwMmoJJ0fvMWtLbSbEQCU+eVx6O8SzmJiIww4nkyUhFmug+rc53qnLCzTzAP27phSu2zvQ+4k4z3YMj2Ppeo+LUE5CArbVB33nutkMQBEFMDiS2LzAmu4Ob2ePxtl4/br3Uh6HRAO6uLAQQzAtdv/0gSnNTbQmJj1t6hCK4ODPJtAkNYC4AeYqIfrERqeBp6/WjoXNIU6T3/S/NmvCiRi+ApiW5cKqzH8sffccQERUJJavrPBn3gSTJcMfFRHzeduc70lSTiTq0RLubJ9/f8TZxfrNVIx47jjQTRbSIKM1NtbxP9E4zD7x2GDdV5GF2VhJuuGOxoYsqQRAEEX1IbF9gTGYHN6vH4zmpLtSfidUU2vFUDrtRO95dUo1/VLJsQgOYR3F5Yx29yIw0+i96/7baT4QWbJGKWbUA0lvZ6SPAeqEUrRQLO6jThCJtUGNnvkX31oZVZbgkzQVvYrwwB9xOasr5yp1W7+/uykLT8zVr6V7z/Ae2cv2jvUAId5+InGYAoCwv1fbTEIIgCGJiUIHkZwir4ktZBr78mPFn1UsLcf3CXFti+8AnZ3HLlj2GfWytXmIZ2TYTarlpLqQLhFqk6SrjLQpUb29HINktmJxMzMaqvvY5qS7cUJ6n6Y4ZLgIdTtia3Vu/vHEBHnnjqMEhw04hcLjc6UgLia3Qz4/Il11d2MmLNRNinYrQDjeOqSiunKqCToIgiM8aVCB5noh21CraWKUDyLK4YG9WVrKwfbboPM2a0MzLSbUcV6RRXH0OtiwDj+2uM83ZjtSCTX2ejV0DtrvsTVW+vXq8ZsJKfe3VvtdXzPSGvUftXB+ze6uuvQ+3VPiwYecRzMlOVubaTmrK+cyd1s8PjwSX5aagOPQZUD914PfNnpNnNELbahzhct/t/P6I9HfMZD4pIwiCIOxBYjtKXAwRpHBiUPSzkuwU24//zZrQhHMjASIrahPlYAMQChy9OFlcEF5cqs/zaGsvtqgs06yKAycr396uwLIScxNdCIS7Pmb7D0hQil3V18fOeKwEdbQXNvr9tfT48eS79WEj5dFquFPgTQz7+2MirjCT4YdPEARB2CO8CiJsYSZ0TncOTPHIzsHFoCs2eNnVYtDsZzMytEIx3HnGxDiwYLoHX5qfgwXTPbaEdqRwgZOT6sK3rirCmmVFWFdVhOwUrcDh4uTaTe/gq4/vw7Wb3sHOj1shSeFTp/h5SiYR//Y+v2EbHkXcUVOJ56svw46aygkvtiI5h3BizuzaRwP1/nNSXaipKsID182HO84BjzsOTgc0AtTOePh1VqPPnY7W+Yx3f5FsZ3U+dn5/XAy/YwiCIAgjFNmOEhdSm2grZmcl49+/Vo7E+BhkJcfDl34uSmrncbPZeXYNDCs/n+wUmgJvIjbftgh1bf2ags7Z2Sma84nUsk4dQXYwBo87DoA44m8WQY12FDGScwhnFzjZ6QSzs5Kx+auLwBjDP7/2sVIwuK6qGAunpxm6j4Ybj9WTgmifz3j3x7ebvbYSjV0DcMfFICslXvheq/PZd6oz7O+Pi+V3DEEQBKGFxHaUmMhj7fOR6232CNqXbt5+XZJknGzvR0PXABJDIiIz2Xie+d4ENJ/14++ffO+8pNA4HAwzvEmWzh9AZOJEND/rqorxh0MtEbt38P2JunFGeo0jOYdwaSyTlU4QrinRo7vq8NqaKw3na8eDO5xjSzTPZyL7O9bWFza9w+p87Pz+EH32zmdNAEEQBDE+SGxHifHm656vXG+zCOncdZWQZGNE2kx8zr0k2XCeD1xXiurnascVQR7v4qK9L7wIjWQBJJqfR3fVoXppIZ7b24DqpYWYlZWMkuyUsN7Eorl78Pr5eGy3/VbwnEjOIVrdEyO9NnaaEp0ZGEYRksdV4Heh5xtH8vTB7HzC/f6QJBmnOvuxrqpY8zQnmqlABEEQxORAYjtKmAkdIGgrZiYuzlf7Z1GE1OOOM3XasBKfNyzKxQ7VeU40gjyexUU4ESpJMhwMeGhlqeH8ROLE7BwWTU+LuJW1aO7ue/kjRXx63HE42toLV6xDyZc322+ki7iJiNPxXhu7TYkuxCLiaCz8opHeEW6hdLpzAGt+8wE87jjFhcfBgLk5yRdMATZBEAQhhsR2FOFCp8CbiNOdA9jf2IW/nvUbGqeoxcX5ysMUidObKvLw6K7jmmYX3KLNbFySDLT2+rGkMEMzvolEkO0sLkRpGWYiVC3qPO44VC8thM/jxlggAG9iHN443Iqc1ATMy0lRCjjNxHu+NzGicRV4Ey3FZzgPZz3n07ptvNfGblOi050D2LDziPB+m4rIdTTEvyTJGAvItpr+hBP1DgfTdH4EoOnw6h+VNLaNQNC6kZrTEARBXNiQ2I4y6j/gd11ZiCfftbaNm6iFmd3InChCOv+SVLhinBrhV7OsGF0Dw6bjcjBoosf82I+vrsB9rxzSpElEEkE2W1xIkoxTZwZwpKUXde192FbbhO7BEaUpzQ6BCK3v6FfOs6XHj027TiDfm4C1y4pxx1Pn8srVjXAijSBbjWt2VrKp+Lz98nwMjQZwd2UhAGD7/qawgvZ8pVKMd+EnmjvelGhVea5yXToHhnFLhc9wv3UODJ93sS1JMg41n8XR1l7cXVmI7fub0NLjj/ip0unOAdz3yiFDXv+GVWWaFJDdx9pwsKkHkgw4GVCal4pls7NMbTU97jjcVJGHWZnJKMlJQU4q5WsTBEFcrJDYjjLq6CBj5rZx/I/5RLyZI4nMORwMV5dkYWv1ErT0+JGTmgBARs3z2iLDTbvrsLV6iXBc66qKUZyVZIge6wWWqOsjh9vCcee67fuDIlUkGqwK7+7Z9qHigawXRiLRuKIsV2m2w8/1vpc/QnFmEhZM90QUQQ43rp3rKoUtvZ9/rwEry6drcm75dm294Z9kTHYh7XgXfqK5UxeEAsH7PM7pUAQpcO5+e+rrlwbTfibYwMUu4Qo6I3mqxD3f1e3QZRnITXMpY23sGkBdW7/i184/S0XTkjRRaf67w+OOEz792HzbIqUomPK1CYIgLh5IbEcZvdALJ14mkiYQyWN/SZLxxpE2jcB45MYFwsXA4EhAaGmWGO/E0GgApzsHIEmy4djrtx/Es/+w2HS8kiTjcEufRnTce80cFGYmGR6bm52fuvDOTBSJRKPTIV74tPb4sWD6uWthJ4IcblytvX6h+CzOTMItW/YatqteWojRgCwUnOq5G0/KQySCVb3AUkdWZRmWY9PPndlY0xJihdeg+ewQGrsGNMLTbDF3SZoLXovFnB2srt+T79ZHFC3m95o6vcMV68Cq8lxl7o+39cE/GoDHHYeWHr9S/zA/NxWSbEwVuaE8z7AouWfbh3h9baXwSQ5BEARxYUNNbaKMunHF9v1NqFlWHLbhBRcqPA/a7h9Qq8f+ekQCo669T9hkIyvUHMbhYJiZmYTPz8pE58AIbvz1Htz06724dtM7qGvvFx77VMeAaeMV/Rg87jgMjARw9zO1woYtVrnPooirJMmo7+hH58AwNqwq08z7grw04blmp0b+GN7OuPTXNCbGgcGRgHA7n8eN+185ZNmcZDwNTSJt6sMXWDvXVeL7X5qNLW/XY81vP8CXH7PfDMhqrInxMcJr0Ng1iNqGsxgbkyz3sX77QfzPsTMRNScSYXb9Ul1ObFldgbZeP+o7+m3tX9TU5qGVpXA6oMz9N//rffzft+uxekk+ckL3m39Uwr5TXZpz4b87zJ6IdfT7x/V7giAIgphaSGxHGfUf35YeP7bWNmLL6gr89hvR6SioxqojnR6RwNhW24SHVpZqhMKGVWXIS01AfUc/9pw8g/qOfjR2GYVPQJaFx3bHx5gKQf0YbijPU1Iq+H7V25mdn4NBUwzJx/q/J87gzqffw02/3ouNbx7DU1+/FFurl+Cxry6CNzEWD14/X3OuD14/H/NyUsPOs/oY9R39Sv6s1bhEmJ1PaygVQbRI4sc+3taHuysLFbHG50u0DSecQNefF49eSzIUBxfRduEwE7OjgQAe1i2CfrR8DuKcDjR2DeAvDV2K4LZa0EQ6Hj3665CT6sL65bORnZqA/Q1duGfbAaGgN5uv5fOy8fraSmy+bRGqlxbil388hu3vNwuj5zeU5ynnLstQnGn+53i7cv84Q4s2NZSfTRAEcfFCaSRR5ny6R0SS7y1qiNE9OIJyXxq2rK5AbUMXAhKw8c1jGA1IGk/oh1aWKo/AOU3dgwbP33VVxWjqHgQgLqzTp3eYRfB4/nJeagJ+vrIUP1JZ9z20shTlvjSlGY9Z7u3ImIwPPzmrGd/m2xZhWyhnPTvVhXk5qWHbyZulROjzZ9XjisTGj4/XLFJvlVscToCFa99ulpYyUYccs9zv9MR4eBPj8ciNC3C0rQ+JcU74xyRsDnlxb3m7HhtWleErZZdYOpxYjcdO2ow+Xeb2y/OFefQbdh5BbpoLgyMBZCa7cKqz35AzzRfPjAHfe+GAMl5JFt/b/OlHzbJi7PyoxZCbvfm2Rbh+YS7yvYm2LCsJgiCICx8my+N7FHuhU1FRIdfW1k71MKKGmYjgr4cT9qfP9OMPH7UaxPHfzPTiZlUeMRAUAzz/mH9fvbQQm3adsxzL9ybgR9eU4HBLLyQ56Pnrdcfh12/XK0Jwhy53XC8e11UV4f++XW849tbqJZiXk4qXDzTjsd11WFGWC6cDWDQ9DX9TmIG4OCeAoH/5tZveEY4dgMYJJifVhZsq8pS24XYXQGbHeH1tJRiD5byLrhmAoItJay+Ot/XhBZWLif6ph9X5PflufdicbbPtd9RUAsC4fmbXBnEsIBvcaZbPy8a+U53435OdeOKdeoNbj/o4osWAfqER7v6yymvnY23pGcJdz9QaxrCuqhiyDI0QXldVjGf3NCiLTvUY9pw8g68+vk/Zx5plRXjiHeO5PXlHBfad6sL/HG3H3Utn4gcvHjA9fzufa4IgCOLCgDG2X5blCtHPKLJ9ERBORNgp6mvp8ePZPVrHhGf3NGBGRqJpBE79/SyVnZ0r1oH1y0vwhTlZmJUV9OQeDci4/5VDihAyy01XR/1jHQ6442Lwr386rhFUowEJH7f0KO4hatG/9RtLsMDnARC+mYpaaEfiba3G7Bgd/UavcTVW12xmZhJmZCRibk6KZcMcs2OX5aYogsxq/DyCu2HnEWXBcml+OnweN/7S0GUavV5c4LX0MDdb9Nlxp8lKcWHvyQ7cv2Ku6fmdDDWBmpuTjNfXBu8VO/fXeDo5BosXjWPI87jxfZUQ5kWN6kWof1RCQ+cACryJhkj89v1Nwm6PlxV4MTQaQEKsEyfaxcfmEfsLvXMmQRAEYQ8S2xcB42k2ohdFOakudA+OaBpiuGIdyElNsHxcz78vyU4ROiGoHSie+vpi25E4WQZinA68ebhFswDYWtuI5fOzTUVQY/cQSvPSFOFmNvYYxzknGDN3Bzt+yuO1wwt3zewsksyOXZRprwkMt3scDUiGxkp5HvF158WdZt1QzRYQZgWN+uizz+PGrYvz8djuOnzv6jnCMRxq7sGmXSc0+wcQ9v4aj4f7tKR44RgGR8bCLkJdsQ588MlZDI1KuLokS7NA6R4cQXFWEl5fW4mOfu2YZ3iTsOY3H+DuysJx3VsEQRDExQUVSF4EROI6AohdKA639GHzbYsMzijzclIMbgoPXj8frx1s1rxvRkaipROCHUcV9bju2XYAu462YfXlM5Ac78T2/U148t16rF9eggJvorIIUOOKdYABSmGcyAliw6oyXDU7AysX5So/s/I7D4foGHbyZyO9Zuo54kV4sgzDNVtXVYxTnfacMgCgsXsQG988hruuLMSaZUW4u7IQG3Yewd6TZyydckTX06rg0u75NnYP4r6XP0JD5xAe2nHEMIZ1VcV4obbJsH8791ckBcNKJP6FDw1jeOC6+SjJTjEtguVf1ywLjvWebR+isXsQy+dlY0dNJZ6vDhZDL5udhZmZwTHztJA9J8/gTP8wPO44225FBEEQxMUNRbYvAiKNrpqJIjOfXpEndLnPE/V8UaumHbzAMC/NHfTxliX883Xz8eNXPlLe85MV87Dl7ZO498sliuCyKkb1pSdiTk0lOvqHhfmzdiKIZvMTrghvPBFxUSrGw6vKcM8XitE7HFBSf7oHR8LmT3PMujZKgNKIJSHWgSWFXsgIPg0xu95Wgtru+ar30dLjV8bgS0+AOy4GD+04oinEVRd0nu4cQOfAMOKcQRtF/dxHUjCs/ozwMTgdwNLiaVgYenIi2ldWcjwkOfgUhuePA7BM/RBdV57/rT521ZxMlOamUW42QRDEpwwS2xco6jSQnFRXRF0mw+UZc+Gy71SnIlj0ImEy8kWtmnbc+9IhvL62UtN4pyI/FY+vrkBrrx/xsU488fZJHG/vFwpWUZ0vj4ZOpEunej9WDVv0+d/jOaZ6MXJDeR4YA0529MMV68Tm3Sc077XrDGLWtfHhGxegpceP373fhNVL8vH3T+4Lm89uJajtnq9+Hy09fjz5bj2qlxZCkoMOOWpcsQ5kp7iw8+NWbNh5xLBwUI81Eicgvej/1VsnkJPqwueLp2F/YzcGRsZQkp1sSAM53TkQ8cJNtPh9dFedUnTMi11JaBMEQXw6IbF9ASISdJtvWyTM/xRhJYrG24kwGoRr2qH3865t6ME3nqtVRIlewI2NSfhzfSdqG7ogycCrB5qxfnmJ4VyiacdoN39+PMds6/ULo/73r5iLnFSXxgXDbl6vWSOd02eCqTiR5LNbCWqrPO/6UMFjVkrwqYBoH3ExDA+8dhg1y4oNYjogAfds+xB3XVkYdqyiXHhRUaf+M5KT6sL/+Xwh3jvdZShqVN9P41lEmS1+F01Pw/PVl5HbCEEQxKccEtsXICJBt+Y3H2BHTSWWFGaE3d5KEIyn2DJa8HEda+0VLgbccTG2RYkkyXj9oxZN4V/NsmJs2HkEc7KNBYR2XVus4KLNbhFepMfMSgnaE+oF5QOvHTZdcNjZp2iux6Tg92Zt7M3Ox2oBoT9fs4Xd1SVZhnQmACjMSELXwDC2Vi/RpInsO9WpFCdGUgAZbgzqz8hNFXk4MzCCLSorSjMxH+kiyuwa5IeeKBEEQRCfbkhsnwfsNNpQM9GmInpBkJ3iQkAC9p3qhIMxQ4OaSPY9Efi45uYkC5t2ZKWcc4bISXXhhvI8OB1AemI8SnNTNXN2unNAEdr8HDbtDlqzqXN87c55OHgUfXAkMCkOEpIkQ5aB4sxkzb75PMzJTsbW/2cJspLjLRvn6O81s0jy3JxkXDHTi4TYGI3A5OczLUl8PpEsIMwWdjzfXL8Ps3znscC5bqWRzr3VGNSfkcGRAN5vPGvrcxfpImqiaUwEQRDExQ2J7UnGzH/4y/NzTLsXjtduTo06X9msOGs8aQkTxeFgKMhIgi89EQvy0tDYNQB3XAyyUuKRl+ZWfKHVublb3jY2cDFbkDgdUHJ8x5MqY9aIhkfRPe44oX/yRIST+h5R28GZ+YPz7plW+zGLJE9LcsHpCOYpm4lx7ngyI2NiC5RwHSztLIZOdw7gvlcOoWZZMbbWNgrTTMaTwsH9sbloru/ox4FPzk7KQup8dpUlCIIgLjyog+QkY9bFb8vqClxZlGFpkReNvGqz4/O0hHxvAh64rhSxThaVCLBd9OfIx9E/PIqk+Bjc/0rQHk49ZrUDh9W85qYl4MuPja8DomjeZ2cla/bHu1FO97iRn+4GcwBxTidGAgF4VQ1cRPvnAjMz+Zzodcc5cUuoi6daYFt1WBSdh1XHyMJpSaY57vMuScb295sVl43fvd8UkeOJGWbj2bmuEodb+mzd37wzY06qC1+7zId8rxsxDgcGRsbginEiIykOFfnppgtXq/t/TnaKckxJkrH7WBvq2votc7YJgiAIQgR1kJxCzCJrtQ1dyPMkCMVMNCJhXNgdb+vD3ZWF2L6/SYlk8zzoF7+5BM1n/ah+rva8iwv14/2cVBduqfBpxqFuzc3HrH6cL3o0v2FVGa4o9Fp2R7QSj2YpB//+tXLN/lp6/Ni06wQeuakMtz/1nmbMW2sbhUWaosXFD5eXoK69H9kp8RpnDG4HV5KTLDyPBpOIcLhIslmO+8OrFmDTrqDbCU9bYQzo6BuGg52Lgkd6D5qlT/CCRzt1A/wpT0uPH0OjEh5545jBkYTbRvL0GvWiJjvFhQevn690I+UWkr99rwFb3q7XNBlaNjsLRdOSUO7zYHBkDL70RE10P9J0MIIgCIIASGxPOmYpIQHJ2r7NKi9UlJfb2D2o+V5toacXr65YB5wOBzzuePz9k+9NSbGkWhiKHDF4ZFfdql39ON9qQTLeNBwzsZoYHyPcX2PXoHDM6jnk16qjb9iwuPhO6Pt1VUVCO7yt1ZcLj/vBJ2cNHRbDnbdVjvvgyBhcsQ6DE8oT79QrKUfdgyO2FmL6e1NUDMkLHvXzLPo8+DxubFldgdqGLuSmJmBFWa7QNpJHqq8uyTLc+/deMwdrriqCf0yCLAO/fvsEVpTl4mBzr+aYPMWpIEP8mZsqFx+CIAji4oY6SE4yBd5EbFhVpinwqllWjNcONo8rF1TUHfLlA8248+n3lO//XN9piBxu2l2HG8rzlOPf/8ohNHSZO2uoj8c7GtZ32O9cGA53XIwyJ2YuE87Q3anPzeVj2neqEwCwuMCr6So43q6POaku1FQVYc2y4L+c1KB4zUqOF3TZLFU6HarHzM+lvc+vuVbvnDhjurjYVtuEdVXGToKi7p5mHRaBoDDV32v8vK1y3H3pwfkSOaE8uit43+iPxVHfH6fP9BvuzTeOtKHAm6jp/Gi306MkyXjjSBuqn6vFpl0n8NeeIVP3FEkORss/bukx3PsP/eEohkYlbN59Ar966wQaOofAWGT52FbdMwmCIAjCCopsTzIOB8OX5+fA445DbUMXAhKUVIMCb2LEj6ZFf/Tve/kjJQrMU1REgsSXnoC7rizEc3sbAABJJhFbLkAmK5oXdJiQcP+KuXjgtcPKcfXjmJWZjM23LUJJdoryOJ/n1h5s6oEkA04GlOalYtnsLI0FXaRpOJIk43BLn+LMwYVtcVawmJN3o+T7czBxAxZZ1kaT1deKn6N+cdHS48ezexrw77eVYzQgoTgrWehdzcDw7a0fmnZYfONIm9Ka3ekAKvLTcUWh1zLqXZGfjhkZwXQJ/c/5/hnTHsvM2q+mqiisdR5gnl7i87g1ntwOpk032VbbhB9/Za7wPGQ5eLyWHvGigqkuvSs22HY9ksLWiToEEQRBEJ9dpkxsM8a+A+BuADKAQwDuBJAD4HkAXgD7AayWZXmEMRYP4FkAnwPQCeAWWZZPT8W4x0NMjANXFmUgz5OA9j4/VpXnKn/kIxWzZn/041UFYpIsFq+NXUNKp7zbL8/H9188YOnuMBme3GqB5nHHoXppIYozk/DjFXPxz68d1uThzs9NMdjcNXYNoK6t3yCKi6ZpH/9Has9m1uXv9bWVyvHV+xsbk5T0Bl5seEuFD1trG5U5VKdLbN/fpMw1vx7q69M9OIJjbX340rxsoVMHd8wQCXy9sFen3vAiR6scd/UTATMhqz6W2ZxJsj0fbNFiSJT69NDKUo1NZUuPH//x1gn8bGUp/lFlG8lTpFyxDuSkJgjPgd9CohxvIHw+djQcggiCIIjPJlMithljuQBqAMyVZXmIMbYNwK0ArgXwr7IsP88Y+zWAuwD8R+j/blmWixhjtwLYAOCWqRj7eBGJv/qO/ojFLE+/0P/RL8g4F6F79UAzNqwq0xTDPXj9fDwWEno3VeQpjgu8GM/pAKpmZ6I071zL6HDRvPEUjKkFGi80dMU6cM8XirFldYWpKwo/VvPZIWXsfDyP7qpDuc8jzLW1i1WL+5mZRu9nvTD8+cpSzMhwY/n8bGHuOC98rF5aiJkZifjpV+bhp69+rGx/zxdnoXBaInwet+kCzMqv2SwPumtgWDm/2VnJ2LmuEq294mi/aP88Z1uUiiOaM7uCVP95OH2mH0dbe3F3ZSGA4OKE52Lz4k0AON7ej84+P7ZVL0Fj9xCOt/Xhub3ncsp56o2Zr7jovO08wSGvbIIgCGK8TGUaSQyABMbYKAA3gBYAywDcFvr5MwB+iqDYvi70NQC8CGAzY4zJF6hvoV0ROp5H0yOBAH60fA46B0eUNIp0dxxazw4CCIqb9ctLcHVJFkpzUzWRw3KfB+19fvT5xzTFeDwSumh6mu1o3nhTTMzOuTgr2ZYV4j99ZZ5w+8GRMdNj2oHna/OU9O37g/Z3IqEoioL/6KVDBqs8vUDrHhxBQqwTv9h5DNOS4vCvNy/EmCTD445FbloC8m10+IykKDTfm4C/nvUrRbDhrpE+4sw9uRf50oQiVX/M7fubxuVBLkky3m88q3lawaPVRdOSlGPw15/6cwPKpqfh2vk5mJuTohHRADA3JxnP3LnY4Chithiz8wSHvLIJgiCI8TIlYluW5WbG2CMAGgEMAXgDwbSRs7Isc9XUBCA39HUugE9C244xxnoQTDU5o94vY6waQDUA+Hy+yT4NIZGI0PE8mp6WFI/hgKQRJvd8cRauKMrA89VaUaSPpPPv99V3Co/rjtPeDpPR9t2qdTUATc4uPw/1sc4MDAu3N2v0wrFaAFnla4uEot1Fkki8nursR1wMw6ryPBxp7VUWTP68VORbFDLyfZulx4iu1c9WluLuZ2oN12j22kpDtF49Zv3+zUSqaDGR50nAuqpiDIwE4GBAXEz4NI3TnQNKN1E+zk2761C9tBAFXjeqlxYqHuA8ip2Z7LLdHn5GhvW9Ecn1jCQ1iSAIgiCAqUsj8SAYrZ4B4CyAFwAsn+h+ZVneAmALEGxqM9H9jYdIROh4Hk0HJGDjm8c1+9/45nFUzanE/Nw0W2PMSok3RCDXVRUjKyVe8z61WGzrDTZfGQlIimiyI1Dstg+3Sp9QH+u/9jbiO1+YhX/903HbgircAshOvrZ2/qwXSSJhyedkRkYislNceKfujEHcz8xIUlqTR5obLIq8nj4jdps51tYLxjDhyKz+mAmxTtQ8/4GhGVG4JjZm99KsrGTMzUlF01m/rc9IJJ899TVyx8Ug35tgGDflYxMEQRDRYKrSSL4A4JQsyx0AwBj7HYC/AZDGGIsJRbfzADSH3t8MYDqAJsZYDIBUBAslLzgiSQ0Zz6Pp9j77ucVm+NITUZyVpEQMHQyK6wZHLUZyUl3oHhzFnU//RRE8j6+uCCsK7bQP5+dsJZT0uc9P//k0qpcWYtH0NOR7E8fl4KIWYZHkawPWi6Rwwt7hYBgYDgjzzhdOT8ORlh7FpUVdyGgnN1gfee3oEz8F+Pivfbhn24GoOMuoj7nn5BmNYOXn1tY7bDn/ZouXkuwUxMQ4bH9GIlkA6q8Rr2lo6ByifGyCIAgiqkyV2G4EsIQx5kYwjaQKQC2AtwDciKAjyR0AXgm9//eh7/eEfr77Qs3XDpfnLHqUHsmj6Wi4IvBueYUZSUIBY2XpxjsMHmnpwc9XluFHLx00CE6Omcjluc3qc7YSSosLvIZ0hTnZKfj8rExbQlG/77LcFNy9dCYOt/Sizz+GzKT4iObUapFkp+h1YGRMLO77hrHxT3WYlZmER25cgIAsI9bJcEmqa1yCWPQEg+dC2037iaQI1uzeNDtfLoLNFi/8aYXdz4jdz4aZfebW6iUYGg1QPjZBEAQRVaYqZ3sfY+xFAO8DGAPwAYLpH68DeJ4x9mDotSdDmzwJ4DnG2AkAXQg6l1yQWHkIR8OzOlquCGYCRpJkHGo+qzhDbN/fBEkGPO44fO0yH7JSXGjqHsRTf25AXAyzdBDhIlfdAhwAugaGTdtyi4SSXtxmp7gQkIB9pzotBSAXig7GlH2X5abgq4vz8YMXDyjz98sby/AvNy3Ed1+wP6dm82cnupqfLrbYO9HRD487Dsvn5+B7qvFtWFWGsjw5YvGnfoKRm5aAxq4hpYuoaFyi+dO3mH/gulLT613gTcTm2xYZPNDNzpeL4GgVH9r5bEiSjI6+YY3rCffmHhoNYElhRkTHJAiCIIhwsAs0QDxhKioq5Nra2ik5Nhd5+jSJaze9YxAcegcLO/tWN3VxMKBM19QlkjGqI5aA0fe7ZlkxklxOTeqDOkLaPThieg71Hf248+n3cEuFT+PlvWFVGb5SdknE9mvjfZ/HHYfbL8/Ho7vq8PCNCxShrb4Oz39jCYbHJIODRaTUd/SHvc6ic3hoZSl++cdjuKE8D0++Wz/h+0Q9D6c7B9DRP4w7/vO9iParPpecVJemlbto3kXntWFVGa6Zm40/HWuf9FbnkiSjsWsAbb3DwusoGp+d+5ggCIIgwsEY2y/LcoXwZyS2zw97Tp7BVx/fZ3j9+erLIoqm2RFzHLMUADPBOjsrGV9+zLjvjTctwD0vGAUq71ppdg6SJOPdE2dQ/VxtROO1im7aPX/9+8pyU1C9dCZkAMfa+pSIJufhVaX4wfZDExaCkSwG1OfqYMDyR9/B3ZWF2Lz7hGG/kd4ndsc1NycZLT3iFBH1Pfutq4rCLgLMrs0Td1RguicBrT1iERwN7My72fiqlxZiTnZK1MU/QRAE8dnBSmxTu/bzgCTJSHbF2PJxDpcjO5EiMC4+zHKp/+Nr5cJ9D40GhK/7PAmoqSpCQmwMJMmY5uBwMMQ6WUQFo+Fyc+2ev/p9OakuQ2oGj2i29PjhinXAFbI9DJfLbCeH2czn2epcJUnGxpsX4lhr74Rz8s3GOzcnGa+vrURH/zkbwuWPvmMqTtWpPfoW86J5N7s2rT1+HG7uwUN/OGbqHjOeBklq7DiRmI1v0fQ02/n/BEEQBBEpJLYnGZ72Ud8xENbH2U50zqx5SUKsE3tOnlGEipX4MBMdifHi7pS+dLfw9eaeIWzadQJb3q4ft5d4pCLLbhGc+n03lOcp6Q/8XDftrsNdVxbiyXfr8ZMV8/DE2yc1cyFaDIS7PuP1eQbO5S3PzUlGvjdR8Z12xTqw+bZFkGVorq/ZHOldZMws9053DmDNbz6wFKfqHGg+z1bzbnZtGrsGUZyZbHqc8TZIUtM5MIy7rixU6gL4kwv1dbTyeCehTRAEQUwWjqkewKed050DONjUY/DGfnRXHWZ4kzR/5M0E8unOAeU9XAC5YoOXLt+bgLXLinHLlr346uP7cO2md7Dz41Z0DgybRiK56FDjinVgeCyAmmXFys94BBgMmmPyxcILtU2m4zQbr8gm79pN72jGLknmqU1W+zN7n1lUdm5OMv7rrsuw/f1GHGzu1cyFejFQ39GPPSfP4FDzWcvrY+f6WcG7HF6/MBc7airxfPVl+ENNJQaHA/jyY+HnSD+fv/ug2XQ8Vk8I1MzOSsa/f60cny/OwL/dYpx3BwsuAuo7+uHzuPHg9aWG++eF2iYMDo+ZHmei8yZJMv561o8n363H5t0n8MQ79fjm0kKsXz4bgyMB1Hf0Q5Jk2/cOQRAEQUQTimxHGX2ktnNgGDEOh1DY6H2c9QKIu3gcb+sDcK4Jib6RyC1b9hqEytbqJaaRSDPXhktS3bjv5Y+UCKEsA1trG7F8fjY+50tXjsnA8O2tH2pynq1SQyZik6cnEucKLhST4mPwxDvGfOOSnBQUeBPxD1fOxEd/De+ZXVNVZJlKEYnHuhU8xaTAm4h3T5zBD7YftDVHetEqyeapH3aeOIiizTvXVaK11zwN5XPTU4UdHzv6h4XHASLzphdx6swA1qvmyOOOw+BowNA2fvm8bGq5ThAEQZx3SGxHEZFAeXhVGeblJttKfchJdSl53UnxTjAwQ6dE/mhd3UhEJFQGRwKmNmhmVnptvX789Cvz8NNXP1aaezx4/Xz4PG6NADzUfBY3VeRBks89rg/nS823Pd05oFj2jVdkhcvtFlnWPXj9fNz38kdh58JqMSDJ4lSKaUku1Hf0Y2g0gHVVRdhWe674UjQv4VrHqzsbHmw6a3uORPMZ6YKLR3mtPNKXFGagvqNfmIayc10l5mSnCBvG8OPro8lq4c8XmE4HEOtwYGxMQkyM9QO4hi5tp8wbyvMMTYPUCxRquU4QBEGcT0hsRxGRQPnB9oNYV1WMmmXFBvs7fb724ZY+Ja9b3UiG70stGEQe0hwuqC6b4TWN4qkFsH6BcP+Kuejzj6LPH8Bju+tQnJmE0lAreJF12tbaRqxfXmI4n9OdA+gcGEac04GB4QACkoz7XjmkCHk7XSijcR0aOofw2O4606Yldj2zt+9vMlzHB6+fj1Od54SnK9aBe6+Zg17/GEYCEi7NT4fP49bMi1l+smh+/zWU9mBnjvTR6u37mwxNbewsMkTnDtiL4rf2+rF8XjZmr61EY9cA3HExyEyOxzN3LkZrrziazIX/hp1HNDaRW96ux4PXz8f1C3ItBXdinLbWwE4xJ0EQBEGcL0hsRxEzATIwEsD2/U2a9IzcNJdlvrZVCoBaIHvccQZBta6qGE6HPXcP0QLhgdcOK7Z+ALDraDuaz/oxOyvZ8N5NIRFbmptm8DPWiye9C8h9rxzChlVlSgpAtHJoRdehoXPI0LRkbEzCxy09aOnxIyc1AfNyUjSiTi9eW3r82FrbiIdvXIDjbX2QZaCrf1iJmAPBFIaBkQA2v3VC+ETCqnAVgOFnv9h5xHb7dnW02uOOw00VefClu7G1+nLIsgTGGAZHAjjdORC2e2m4Qlx3XAzyvQma9uzqRcCxNnFhpsj+sK03eG9tvHkhvvbEPs353/fyRyjOTMKC6R7T663vlOlk4Ys5CYIgCOJ8QWI7ipjlwTpYUKhx8eqKdWBVea5mW7spANOSXBrB1tLjx7N7GoJdAlMT0Ng9hGf3NGCRLw0FGeGjeGYLBO7q4Ip1ICAFReC/W1gDihYOd11ZaOoC8qu3TqChcwi5aS7siHIOrR3HkrExCS8faNakluijqKJUi1sqfPj5jiNKmsiaZdo87nApDFYRY1mwwGroHEJGUjz+5aYFGBgewyWeBCwp8ArnSHE0WVeJ9xvPahxNeCoHf6qw8eaFuLokC43dg8J0Fv25qwtxrfYZzgnHyoHk4VVl4mh5jx8Lpptfb3WnTEkGEuOcpmlDBEEQBHG+IbEdRczyYONizqV6mP3ht5MCwCPWvL00p6XHj027TmDNsiL86q0TEUXxzISpLJ9zk3hubwP8o5LhcT1/r/pY6nbY09MSwgr59MT4iHNow9kF2mnbfbilRxOR5lHUWZlJKAtFUUXFqDXPfxCKhAdzi32eBE2edrgUhnALAdHPPv5rD16obcJNFXlwx8WgoWvQtCmMw8EgWJUs3wAAOmpJREFUyVCEtvrc+CKHi98tqyuUhkP66LP63LtCRb67j7Vr2pzf9/JHwtQcO7n4IkF+sqNfeP7epHihj7v6nJfNzkJhRpKyaPN53Cj3eagQkiAIgphySGxHEbM8WABho7d6gdg9OAJ3rBNrriqCf0yCLAPP7mnAwulpYQVyJFE8kTD92cpSdPb5cdeVhZrGL4nxTksRq49Wrqsqito4OXY8me04lvy1RywIW3qGUaaKoqpTLSRJxvrlJcL0mHVVxXh2T0PYFIZwCwH9z9ZVFeMPh1rCtkpXE+5pBf++tqHLMvrscDAUeBNRf6Yfbb3DGp94vggbGg1gcYFXU/iamSy+PxmCRacF3kRD06EbyvOQEOfEz1aW4h9VEfmaZcX47gsfYv3yEkNreP2CS79oo0JIgiAI4kKA2rVfQKhbePMoqj4ndsvqClxR6MUbR9o0omzDqjLkprmQnhgfcRRPkmScOjOA+jP9GB6V4I534JPOIfx851GN6Fk8w4NyX7ppS3V9O+ycVBduvzxfE523M06ryLVZy+2t1UswOBKw3X3wzyc78A9PG9vIP3lHBf6maJrlXB1qPquxW+TbPnPnYmSlxJs2ktELRdEcSpKMv5zuwjsnzmB2VjIe2nEEN5TnhW2VrsZsjtR5+PrvOfq28PUd/Xj5w2ZNsS7fvnppIa5bkGvIz9582yKMjMmGRcOze4I2gBtvXojZWcn48mPvwOOO0ywk8r0J+Ke/m4+PmnswNCrhd++fc7vhtQGAsZA00iY4BEEQBBFNqF37RYI+inrPF2drigdrlhXj/lcO4amvL8bVJVnYWr3EtLgvUvSC6Z4vzsK6qmIMjAQ0fttWRXX6iCrPJ3/yjgo4GLMlhM0i1zy/+HhbH+6uLFQsB4FgVHbX0XZs2nUC+d4EPHBdKWKdDJnJLiXtRn/snJQEYZrOJakJlvPkcASLDEWRYxkyCjKS4EtPtIyqW82hw8EwLTkeT7xTj7srC9E9OIL4GLFPe1uv2F1DFD3X2+9tWFWGjW8e02wnSj9q6/WbFuvOykqG06Et6vS443CwqQeXFniwtXoJugZG8MEnZ/HsngblenGLwI03L8TR1l5srW3UdH/8ye8/wk2fm65ZCPBrbFaoG86fnSAIgiCmChLbFygOB8MlaS6NgwlP6egaGMbR1vBuD3YR5c9ufPM4qpcWYvPuE7ZTPkTpLd2DI8hOSdA074l0LBt2HsFoQDIsPNQpLgEpGEm/pcKnyUPWR1T5HOV7tUV1DgYUZyUh30ZaS7i8aysxbac9fbATY1Ac/2j5HGSmio83GpCFucwOB1MWY229fngT4xGQZWy6tRyjgQAykuIxFpDx/avnoK69D9tqm5T5EdUSmKXGlGSnaOoHclJdSpR6067g/D+0slTpNPqtq4oUQX2mfxjL52Uj2eWEK8ZpcKyZnp6AnFSXxq88XKEuWfsRBEEQFyLUrv0CxpsYr7Sg/tVbJxRhGet0TKi9tR6zHN+8tARsvm0RXl9baUvIi9phr6sqxqnOfssW7OHGsqIsV9MhkLua3FCep4iz373fhBvK8wzuJ4/uCr5PP0e8qO76hbm4ssiL6xfmYtnsLFuLlfG2/bbbnr6xexCP7a7DirJcZKa48Is/HEHNsmLN8e5fMRf3v3JIeM0lScYbR9pwy5a9qH7ufXztyX2oPd2NB177CIMjEj7+ay9ePfhX/GzHEfzft+vx/S/Nxs514mtc4E1EaV4q1lUVG853RkaisvAAIJz/e186hNsvz8fqJfnKvfzqgWb0Dwew71QnEmJjhI41DAw3VeQpx+PX2D8qITE+Rjkmh6z9CIIgiAsVimxfwIjSATasKsPAsDiNwSqyZxVRzUpxId+bgBVluUrk8dUDzZidnazxzw6Hw8EwNydZ066bR5XN8ov1iKLGToc4jaEsNwVbq5coDiFmTiD8nPRzZMeH3Ow8x9P2244lHhBccDR0DuFXbwUdZho6h/Dc3gbNU44+/ygaOoeE11x0nE2767Dx5oWaqD9/OnDvS4ewo6bS1N1k2ewsFE1LQrnPg8GRMfjSExU3FPU9ajb/Bd5EfCc0Hv704Zv/tR/+UQnfu3qWcJv6MwMozU1FTVUR4pwOMAas+lwenAzITokP6zZDEARBEBcKJLYvYHg6wJbVFaht6EJAAja+eQwPXlcaUdOOcA4ePo8ba5cVGzyn5+WkRpyWwm0I9dh9xC9aYFyany483zR3HJLiY3H/irlY85sPlNdF7if8a7vRz3DpHuMR6nYs8SRJhjvOiZqqIkgykBDrgCvWYfBpX3NVEWqqijA4ElAcPsJ1fzza2msQ4LxI0ur6OBwMBRlJQt929cKjo38YT7xjLKRMT4xTXjNEv8ck4TUbkyTM8CaCMaCurV+TWz87OwVXl2RF3Z+dIAiCICYDSiO5wGnsHkT1c7VK3utXFuTiSEsPHl5VZjuNwSyiylMQGrsHhZ7Tjd2DEY9XnVbA4SJXkmTUd/Rjz8kzqO8Qp5Zw8bajphLPV1+GnesqkedJwEMrSw3pKd/e+iG+/Ng7GBmTsXNdJf52VgY26OZlXVUw/SCS6KfddI9ozo36uLds2YtNu07giXfqkRQXg3u+OEtzTt/5wiykuGKw5e16/MPTtYbxmR0noNXfStR/vCkY/HruO9UJAPjcdI8wvSYrJV55TR/93r6/yZAis66qGPleN/xjAczMSBI2CWrsHkThtCQsKcxA4bQkEtoEQRDEBQtFti9w2nr9Bns0V6wD/3LTAry+thId/eEje50Dwxq3B+7kwaOZdiKudvF53EokXpKD6Sjrl5fA53HbtmvjUWN9W/rqpYUozkxCU6hLptrdYkdNJSoKvCiXZJTmpqK9z49pSUE3kkW+tIiin3bTPcKhj477PG7L9AfRcX++8yhe/OblqJoTjOK645wYC8j42pP7TMcnejpw/4q52PL2Sc34XLHB7qbR9DsXRZyBc/7h/Lh87C09fmytbcTGmxZgYCSAjKQ41LX14ZE/Hkf34AgevH4+PO445Vrz86ViSIIgCOJigcT2BU5Wigs3VRgLz777wgG8vrZS44msh/tnt/T4EeOA4jpRs6wYW2sbMS3JpRwjkrQU0XG4qBwLyNi06xguK5wGpwP457+bj8tneNHYPRixgNW3pd+064TiD20mvkTpHTz9gUdirZxAgHMLnBvK88AYkBQfFLjH2/qC+9NtJ0o5AcRe0GoxyhcDvBmM2aKnf3gM83PTFEeXPSfPWC6O1KkdDZ0DGBgOoGtwGDVVxWjoHIQkA04GzMpKxrxLUuBLt5+Cwc+1o29YeD25FzZv287PjZ9318AwijOTNM4y93xxNjb88ShWlOXi/lc+0pzbfS9/hOqlhZrUJCqGJAiCIC4mSGxf4BR4EzErM1koro609pq27RZFHu9fMRd9/lEMjQTw4xVz4XScO8Z4C870x8n3JuCbS4vwT699rCnqvCTNFXH0XC8+lRbp6QlYs6xIidDbEV9mkdi5OckGH251Mx6POw63X56PzaE25/qIvGi/G1aVIS8tQShGd6iiz/rtHl9dISxU1Z9buMWRWvz70hNxqrMfT75xCrctztd0gdx488KwQlu9r5xUl9Kw5+7KQuH15F7YcTEMa37zgWHOCqclaZ4+8NbqsU6HJqdcvU+fx62cLxVDEgRBEBcbJLYvcBwOhpKcFKG4ggw0dg1oIrdcGLnjnAax98Brh3HXlYV48t16/HjFXKQmxMKXHoxATkuOi7gLI2BMfVhRlqsIbX7c9dsPYmv1koij52pRqfZwVrtpbK1txPrlJWHF1+nOAWzYeUSTTrNh5xFctzBXiZhzQRiQoOQJ31CeJ8wZzg1FcEWpH+u3H8Qvb1xgubjQb+dxx6Ghsx/fuqoYP35FW6jq87g1+7FaHJktKh67dRFuVnW9tPNkQb+vmqoiTSdJ0fXkXtjVSwtNj8VdTIDgggoAri7JQm5agrBTZWuvH3ddWYiy3BQUZyVTMSRBEARxUUEFkhcBMzISDQWCNcuK8cs3juL9xrOQJNlQ1LfraLupDZ5/VMI/v3YYcU6Hss1Nv96LW7bsRffgaERiRh99NrN/GxwJROxNrfazFnk4b9pdh023LrLlAd45MIxbKnyK1/MT79Tjlgof3HFOZX+8aLS9L5hG8q2riuDzJJhGcHd+3Gqa+jE4MmZZDKneji8k2vtHFKHN9yMqVNUXke6oOeeRbZZvfmZgxFT8m6Hfl7qTpKiwUe2Fra8lVR9LVID6xpE2zMtJMdwjNcuK8d/7GvHku/UozkqmYkiCIAjiooMi25OMnY6B4XA4GMp9aXjkxgU43t6HgHSum+S9Lx3CwulpAGAQRlY2eP5RCZ0DI4bo6tHWXrhiHSjwJgrHqj+fHJPuhvrvs1JcuGyGF3PXVaKtdxgDI2PIT7eORqtzj4+39QnF4tBowNZ8xjkdQrH+8I0LNPtr7/Nr0kjuriy0jOBurb5c+POOvmHULCvWROLViwt11J4vJMxSM0Rt2c2sB83EP28EE8mTBdG++D5aevx4bm8DqpcWIi8tAQ1dQ5qOnvpLoj6W2YKALxpmr63EkdZeHG/rw3N7G0y7WxIEQRDExQCJ7Sgicp9440hbVNqq+9IT8VFzr8HDmgtEWdZGlHnkUZ928dzeBgBB8eOOizFEV/XiUD1WdVqBxx2HmyryMCszGU/eUYF7XzqEhs4hvHqgGQ9cN18pdNOLTJ7za3c+uKjkYx5vEefgiLgR0Okz5zow8v2p00is5tE/KmE0EDCkdKjneV1VMYoyk5DiikVWSrxyLLNmMKJzNGvLLsIsnzsrOfJGMPp9bd/fhHVVxcrcdA+OICc1AUMjATz5rjYXPC6GKfnnTgdwaX66kg4Tzv1mZmYSZmQkYm5OCq6Y6SUfbYIgCOKihsR2lDArlNv45rGI8mTNcDgYSrKTLQWnyFJtW/USdA6MAIzhp7//SIk8qv2P1dFVq7HyiKTIivDnK0uR6o6BNzEeJVkp+Fy+x9BwpL6jf9yWemZ5yg4WdOewemoQbBQjjuyOSedELs+P/ktDl2YeeffG/HRjBDc9MR7lvnTMqalEW68fw2MSfhKa53xvAqYlx2Ptb42FguqofffgCBwMcMc5DQuVmmXFuP+VQ3jq64vH3RSIF0L60hMj6nqp31f34AjcsU6suaoIIwEJszKT8Un3IJ7dc667ZWVRBi4tSIckyRj4YkDjOsLP3Y77zXi7exIEQRDEhQaJ7ShhVijHO/Rx1BG8SJAkGU09g/jJV+bhn1495/TBBaLDwYTeyk1n/dpIdFYySrJTMCND639slmutHiuPSIqE+Y9eOqQUX6qdJ9RMxM9bLU67BoYR63Cg6ewQXvqgWbE0FEXJ+SJow84jhgj1d74wCwwyHrmxDK29fjy2uw7lPo9BDLb0+PHku/XYsroCP/79xxrxyAVrgTcRR1v7sGHnESWae3mhF3c+/RfD4mL22krMzEzSbMcLA/O9Cdh480KcaO/H8JikCHu790y4VvKRCFi+L++di/HOiTOQZeDXb9crtouP3FSGZ/c0nHs/A1yxwRz4xu5BRWirz31OTeWE3G8IgiAI4mKDxHaUMBOSTl0JqlnqQ7jc7tOdA9jfcBavfNisRBFlGYpALJyWhKtLsrC1eglaevzISU1AiisG12x6R8mx5a4bO2oqlX2LWm1ziz2nA0iIjVFSGLgINRPm/HWzaPVE/bzV4lSUtiE6bmPXAI629uIrC3LhcECJyl5e6MWBprPo9Qfw1J+PKwKyvc+PxQVeoRi8otBr2iL8k+4BSJKM//fzRXDHx+DxUAMZ0TypLRv1i7SGziHcs+1DzSItUl9pO1Fhu7UEDgfDtOR4YRv27FQX4mIYbqnwKYuYLW8HF1sed6zlwspqQUAQBEEQnyZIbEcJMyFZkZ8u9AhWi53MZBdOdfYLfYm5AGnr9UOSg2JMHSkHggKxwJtoyA9/aGVp2O576m6NG29eiA07jwjF0/J52SjwBl1RGjoHwhZfiiKxdiOaVkJQ9ARh0+46RZyqjytJMt5vPKvxluZe4+19fvzbn+qEwt8qOqwXsZIk45PuAew71a2x7Punv5sHxmThPB1v68PcnBTL7p18kTaeOQr3czN7QLPcebPrtqTAi023LsItAktBs8JR3kiJ0kQIgiCIzwoktqOESJBsWFWGJQXpwvbVerGzrqpYEcai6HBWigtOZl4kKBKhj+46jn+8tgRHQ50Pt+8PpluIoqRcYOamuYTiiY+l3JeGM31+3L9iLh547bBp8aXVMawimuGEoJk4ZaG5SYh1KpH4050DuPelQ5pzeeC1w6heWojBEQk/Wj4HP995VHO9+PWxGx3e+XErJEk2WPb95Pcf4/HbK/DLGxfg+y8eUFJE1i8vwYn2fnT0D6PAm2i6SKuak2laHBhujsL9PFw7epFQN7tuZoWnsixhy+oK1DZ0QZKDzXluvdSHU539po2YCIIgCOLTCIntKOFwMFxdkqUIjIAEbHzzGGKdDkP+sqhQ8NFddZrUAX10uMCbiNK8VI0bhDrque9Up0b05KS6cEuFD98LCT0u6IuzkkxzY63EEx+LLz0ReemJSoOYhFgH5l2Sgp+++rGm+NLqGFYiNpwQNBOnDgbULCtGzfMfYP3yEiyfl20qzCUZ+KdXP8a6qmLcdWUhnA6gIj8dVxR6IxKBPEWlMCNJeJyWs0OYnZ2M6qWFcMc5keyKFbZvF0WNS3PTTMcSbo7C/dwqd17U2VKdg28nNSjfGywkVRdH/njFXDz/XiMe3dWvdNEkCIIgiM8CJLajSGP3IO5/5ZDSbvsrC3KxYecRzMlO1ogLM7HjS09ATqpL2ILc4WBYNjsLRdOSUO7zYHBkDL70RCVKqBc9oiLGR3fV4fW1lZaCMjPZOq9aiU5nJ2vabT/19cVRyb8NV0Tp87ixYVWZRsjdv2IueodG8eyeYDEhF5ZmwpzbJM7JTkZCnHNcY1anqPzyxgXC47jiYjA4EsCc7BQcbe3VpK1wAcy9pSPJX+4cGNZ0wuRt6xs6B1DgTQwrpt1xTtRUFUGSYWh5H06o6xE90XngulJUP1er2cc/v3YYa64qwsHm4+MqECYIgiCIixUS21GEdynUezJ3DQxrxIWZCGw+O4TVS/JNW5A7HAwFGUlKe3Y1etHjdIiL8zr6/ZiZKU4VAIBTnf2m0XP1OPRRTvX3kiSjvqN/XI18zOYmIdaJPSfPYCwg47k9p5SI9CKfB//6xjEcbO7VnKdZoSNPd3HFOpDvTRy36FOnqDz+9kn8ZMU8pU29K9aBn6yYh2f/XI+Hb1yIy2Z4DeekHiefOztjkSQZfz3r1/ha87b1H3xyFkOjEmZniS0ipyW5DFFrfct7/RMS/Tj1iFKDGjoHhPuYlhQfcbEnQRAEQVzskNiOImZdCrdWL9G8TxQN5CKwe3AEW6uXWKYRiNCLnoTYGKUwkOOKdYCB4WR7v7Agc25OMtb85gN43HFK5NTBgJLsZFtjkSQZjV0DeL/xrCJEI23kI5qbB6+fj5rnP0BD55BmrnhEtnppoUZs6wsdJ6MjoTp6fLC5F3ivAQ/fuACyLIMxhmf/XI9/uHKmxhpwIk4snNOdAwZLvU2767D5tnKcaO/DsdZezLskWZia4nRou4x63HHwjwVw35fnojDjXG0Ab0bDI+evHmi2HKd+8dXRNyw818T4GLL4IwiCID5zkNiOImb5zoMjAc1rIv9iLh4BmLYgD+dAoRY9kiQbBNe6qmJ8e+uH6B4cERZkPnPn4mCucY9f43gyKysZ+WGi07wo72hrr0bk833PXlsJxmDLak67aHAqQpvvT+0+4h+VMEsVydVH4h0OZqsjoV0rPI4+An+wuRc/ePEAtn5jCYbGAnj4xoWafUTLW9osReRoSy8eeeO4ErH/u7JLDIW56qi1WcfQL8zOxPeuno269n5IMuBkwPeunq10f7RDVkq84enIuqpiFGS4MTcnlYojCYIgiM8UJLajiGmr7BSxM4eZf7GZD3ckdm1q0drQOYAPPjmr5DQDEBZkDo6MmVrVzb8kBZJsLpZ5ru/dlYUGMehxx6H57CBqG7oVZwpexGg2dr5o2HPyjCK0Odx9hI+vJDvF1P9atE87c/vQylKU+9LgSxeLbjPxXJonfiJhx4nFDmadMIdUi5t7XzqEBXlpmJmZZJq+ZNYx9MVvXo6m7iGNXeK6qmI0nR0Upi+J8KUnojgrCdVLCyHJwacjxVlJJLQJgiCIzyQktqNIpNHLSN4faeEacE5gtvUGG9qoUQtWICjYpnvc+I+//xw+aDwnim+p8GHnRy0o8CZapoaoI65qMZiT6sLtl+ej+rn9mpQZUeGoCKsiRz4OXiQ6kfxr/dze+9IhVC8txMxpSfjy/BzExDgM0e+rS7LCinw10fCWHgkEDJ0w1baLfPyNXQOYmWnuc27WmKh7cFSJSPPXHt0VbJxkV2zzYt7CjCRqWkMQBEF85pkysc0YSwPwBID5AGQA/wDgGICtAAoAnAZwsyzL3YwxBuBRANcCGATwdVmW3z//o7Ym0uhlJO+fSKtzK7s8/vXGmxfidNeAJo/7/hVzsfW9RvztnEyDX7Ve6HMXk+37mzRi8KaKPIN442kgdsYuch/ZsKoMuWkurCrPjYqIs7IIXL/9IDzuOFxR6DU0DVJb4kWahjJevInx2FrbqOTUz85KxiNvHNU0LnLFOuCOM3601febumOoejv/qFkq1FhE46SmNQRBEAQRZCoj248C2CnL8o2MsTgAbgD3Atgly/IvGGM/BPBDAOsBXAOgOPTvMgD/Efr/giNSkWH3/eNpdc4FYOfAsEGw8oJInsPsYMDyR9/RiGLeAGZWZrKl0JckWeNi8tzehuB2WclwxzqF2zodUMZuJlQlScYbR9qw8c1jBj/smBiHrfm1QziLwNqGLqQmxJg+WbDypo624C7wJmL98hLlWPneBHzz80X4p1fPOaGsqypGVkq8cHt+v/k8bkPTmfXLS1BoUsjpTYxTmgURBEEQBGGfKRHbjLFUAEsBfB0AZFkeATDCGLsOwN+G3vYMgP9BUGxfB+BZWZZlAHsZY2mMsRxZllvO89CnjEhTVPR5yPneBGxZXYFYJ9MIWp4asOfkGaEoXjQ9mLdsJfRPdw4YXEwAKHneom0r8tOVtvVmQlWd3sFzy/O9Cdh06yIMjgQijiCbiXordxhXrAMBCUohqX5+2vuCEeVIU3zGi/5pyLQkF5rODmjyo2dkJKKjfxiSDOH88EWMvtvp1SVZcDiYYS6+84VZWKtqFkSCmyAIgiDsM1WR7RkAOgA8xRhbAGA/gHUAslQCuhVAVujrXACfqLZvCr32qRbbkeYIq9/vjovBhp1HFAHY0DmE6udqTbv3mUV3872JYYU+T8PQu5hcMdMr9LresKpM6dZY39GvdKPkIp3nc+vTO3hXTN5OnhcyTkuOQ16aG5IMtPeJ0zisRD0AzM1JxjN3LkbXwAhOdw7g2T1Bi0DuQ3313CzTBcdEUnwiQX8/LC4IzuGMjERM9wSb2YwGZNz/yiHFJlEUYRflqK/ffhCluakonJaE5fOykVu9BLuOtiMgAU//+bTSLGj22kpDLjhBEARBEOZMldiOAVAOYK0sy/sYY48imDKiIMuyzBiTI9kpY6waQDUA+Hy+aI31vKIWVGMBGY/uOoaqkmxM97hxrLUPszKTFJGl307UsERtKWglAK0EdbjccqsUF1Ek1ukA/tLQhawUl2UjIL5fjzsON5TnYU52Mr4faj/Pz+felw5hzVVF+Ki519CIRy0yzQpM566rxOGWPs15/2xlKe68Ih89/oDS8KUkK1mYisMXHNHw0LYinBsNv6bXbnonbIQ93OLA4WAYHAkIi2qPtPYqBakEQRAEQYRnqsR2E4AmWZb3hb5/EUGx3cbTQxhjOQDaQz9vBjBdtX1e6DUNsixvAbAFACoqKiIS6hcCakHlccfhzivycfvlM9B8dgjfC4nMSKKVaj9qwFoAhhPUVrnlZvm/aq/rwmlJwtzmp75+qWkjoAJvIjbftgh1bf14dFed0FbQPxrsTPiTUM4yf02dT326cwDH2/qE27b1Dhvm7R9fOoSt1UswNBrAqvJc+Dxu09xxszSUaDdvseNGYzfCbif/3+w9x9v6MDcnhQofCYIgCMImUyK2ZVluZYx9whibLcvyMQBVAA6H/t0B4Beh/18JbfJ7AGsYY88jWBjZ82nM1+aCalZmEm5Z7ENbrx8nOvqFTWLsRiudoTpC3onRqjnJeBwkwuX/is5PfS77G7pN3C+CjX1meJMUhxR+HnoB6I6PMRHSfhxt7VP8v0XbDoyMCbcdGg1gSWEGAKC+o9+QO+6KdSgpOdHy0LbCjpC2W0RrZ3FQ4E3EQytLNXaP/EnJFTO9JLYJgiAIwiZT6UayFsB/h5xI6gHcCcABYBtj7C4ADQBuDr13B4K2fycQtP678/wPd/Jp6/XD447DN/+2SBGIgNgP2W60sigzGWuWFUGWgcd2B/2SoymUwuX/6s/PcC5jkmUjoPa+c9vobQW5AGw+OygW4XFO3Pn0X+AflYTbbrx5IfLDFH+ajtskJUee4PMUfd59r38EcU4nkl0xyPcmaBr8uGIdSIh1Ki4hdiPsdhYHDgdDuS9NKbzkXU67B0eimh5DEARBEJ92pkxsy7L8IYAKwY+qBO+VAXxrssc01WSluHBTRR6OtvYq4s7J7OUDmzlq/HzHEY0Hc7SL9iIRoqIFwasHmk1zoSVJxlhAVrZp6fErtoJzc1Igy8Avdh7ByJhsaA++8eaFGAlIyrH4tnddWYiy3BQUZyUrIjScQA0XMbbb3VMtpDOTg7nrLT3nCjoBCPPut9Y24tZLffje1bPxyBvHlOLHmmXFqNG5hNiNsNt5iuFLT8Sc7JRJTY8hCIIgiE87TJ5oKO4CpaKiQq6trZ3qYQAwt5wT/Xw0IOFwSy/+7U91wSj30kIMjgYsi//0+zne1ofRgIxH3jhqiISauZGMd+z1Hf2aojyr45iJ0qtLstDYPWgQiPUd/bjz6fcMBZQbVpWhLDcVpzsHkBgfg5GxAPI8bvQMjqKl14+cVBfm5aSisXvQ1tjGxiR83NKDlp5z26p9vMOJaT4HvJCTseAiaeWiXMVaUbSPe744C0/972l0D44ovudqr3M+3ruuLMST79ajemkhlhZPw9t1HQhIwO/eb0JLj99wTtrzScC8nJRx+5Lz60+dIAmCIAjCHMbYflmWRUFkatc+2YSznGvsGsD7jWc1ubH3fHEWfrR8Dn6+8yh+/XY97rwiH7/++89hLCAhMzkeAVnGoeazGAlIiHM6NJ7TXHCZiVRRVNKqqYx+7JtvW4QZ3iTFYs/ncdsuDrSKvIqirG29fjR0DikRacaC6QwJcU5cExLRfEwnOvpxsKkHkgwcaelFR/8w/rY4M+zYRDnn+sVMuIgxT/9ZvSRfM9/53kT40oPvO3XGmG6z8c3jWHNVER554zju2fYhnrlzsfApAW+tLsnAmf5hoUsIf5IwNibh5QPNuO/lj5RxPHj9fFy/IHdcgps6QRIEQRDExCCxPcmYuUjMXluJY219ONraayiA3PjmcayrKlacL/LSE/HM/57CVxbm4oHXD+OWCh+21jYaxDQXibzLIPev1rtnqLHbVAYAPO441LX1a1q688i0lf+3mkjEG0/fUPt3u2IdqF5aqJmv+vZ+BGQo88i7KBaFPKOt0irsuHyEGzdP/9G7qtz70iHMnJaI0tw0NHQNmDqp8K8HR8ZMO1m6Yh1wsKDXuFVKy8ctPYrQ5vu97+WPUJyZhAXTPWHnnCAIgiCI6BK9nteEELOc5sauoMiTZHEBpDcxDowBAQl44LXDKJ2ehntfOoQVZbnYtLtO+V8vEk93DiiR2Ke+vhiVxV5cvzAXVxZlCCObarGZk+rCXVcW4mhrLw4196BzYFgzthvK85R0FvUxG7sHUTgtCUsKMxR3jmjA89BdscFx8yY2L9Q2ad6Xk+Y2jOvRXXVo6x1WRLLZ2KxyzoHgYqS+ox97Tp5BfUc/JMmYdlXgTTRtab/raDt2ftwKV6xDOQ8Od1LhX/vSjedbs6wYrx1sxrqqYpTlpWJeTqrynpxUF2qqivDIjQsgy8GxmnW6bFXl7RMEQRAEcf6gyPYkY1Zc5447Z1cn+vklngQ0nR2CBODmijzMuyRVk1LA/1ejTiewG0HmYjMn1aVJg9jydj02rCrTOGCEO+Z4CJcTzjs7Do6MwZeeCKcD6B4c0exjyMS+b3BkDPUd/ab7BsTXJ9+bgIRYJ/5yuhN/Pes3FG/q8+UdDoaSnBThdQxIwVbum766yFDEua6qGE3dg8p+Z2QkYkZGIubUVAbdSGKd6BsexefyFyArJV5JSVk+Lxtz11Ua0o8eWlmKAq9bOI7sVHIQIQiCIIipgMT2JGNmx5aVEg9XrMPUks4/GtCkRXDhC0AT+dSLqoRYJ/acPGMqLvVwsXlDuTENYuObx/CLG8qwp74TkgykxDuj2ikxXD67WTGlfj5zPQnCccXGOJQCSTOhrL8++d4ErA25fHz36jk42dGPuysLsX1/k9KyXJ9iAgAzMsRuMM/tbYDHHYdAQEZWiguP3LQATd2DGBwJYEZGIjKS4rAj1HyHjyvcIsnhYJBkKEKbX697XzqEe6+Zg4dvLMOJ9n5IcrBQsygzCfNyUsd1jQiCIAiCmBjkRnIeEDk6ANB0i7ypIg/zc1ORlRSPrsERfPjJWWyrbVJs+1yxDmxZXYH7XzlkmrP94PXz8djuOsUazsy1RD+2nR+34mhrr6bwTh/pdsU68PCqMrjjnYac7XDHMMPKyQSA6c94V0g+nz6PG3883IbvvvChZqz/8uYxW24s6uuTEOtEzfMfCFvIP7e3AS09fjxffZnS8Ea/n0PNZ7HraLviFgIAt1+er4lo379iLuZkJWORL5hDbRbZt4r67zl5Bl99fJ9hDD+8ZjZinQ788o/HlOP9y00Lcc388V0jgiAIgiDCQ24kU4xZSoe6eC87xYXDLX245fG9QoHnH5UQ62R46uuL0TUwjL8p8mIkIGFr9RIMjgTgjguKRC4uzQr9RGNbPi8buWkJmkJNUaT7B9sP4vW1lUoxZHaKCwEJ2Heq03YkXY1ZvnRbr1/5Wv8znrKink9JkhEfy5QGLA4GOB0MI2OyYfuugWHl2Oox8/3tOXlGmA+/aXedYsFn1fK+NDcNzWf9SoS7pqrIkE/+wGuH8dTXL7V0QgHEkX2+sDFLT8pPT8R3dAWf333hQ5TkRGb5SBAEQRBEdCCxPYWoRZ66JThwTuBxazjeVdEsxWDPyTOaKC7fh5m41I+jNDdVkwbhdIjFbke/H0sKM1DgTbTVyMUKd1yMaefHZFesrZQVHk1Wt3Tn761eWqiJ1ud7E9B81o+/f/I9Q2pKQ9cgGroGkBQfg1SXU3juTgeEtob6CLTanWVwOCDc1/6GbrjjnKZOKAAMP9uw8why01wYHAkgJ9VlSFv50fI5pu3no93MiCAIgiAIe5DYnkTCFf+pMYvyZqe4kO9NwPrlJZad+8wK/UTiUiSI9V7SCbExmkg3oBW7di3zrBgJBITt10cDkq3W4+oUGNHczcpKVubEFevAA9eVovq5WsOYt6yuUF7n6Tii1uhVczJRmptmaEhktujgiyjRomFoVDJ1Dmnv80PWudTkpLpwS4UPt2w59+Rj822L8NqaK3G0rQ/H2/rQNzyGrsGRqObVEwRBEAQxMUhsTxIiEbZhVRkuSXPBmxhvEN5maQGN3YPYdOsig8jTIxKnZuIyN5R6ol8AqCPtkiQb9vfQylI4WPDcImnTboY3MR67j7bi4RsXYGh4DO74GDzz53pcWxr0+Pa4Y7G1+nKMBgJIT4yHz+PWLF4cLBj9vbuyUDh309MS8O9fK0difAyykuNNxW1tQ5dmju57+SNsvq0ca37zvkZAi66ByIv8aGsvXDEOFGQkwudxG9rR8xbsV8/NshTG6p+J0nrW/OYDvL62EqW5KchMikd73zB+tuOIYQETdCmhFusEQRAEMRWQ2J4kRJHf9dsPKnm/+ghzgTcRD60s1Vi58ZztK2aKm9Hoo+b6Bi5mgnjX0XZs2nVCGOlW73duTrImcvrLPx5TWov70sUOIFYRVP2Y81ITcOvifPzgxQPKOf/yxjJ8/Nc+TbHjxpsXYmGex5Df/NDKUnjccUJHlwevn4+arR9oikVnqyLd6jEHtFME/6iEw3/txTN3LoYM2bJRj3qORUWlG1aV4UtzsuBZXYHahi4EJGBrbSPWLy/BvJwUy+i9nbSeo229aOwcxKO76nB3ZSG6B0c0HTcdDCj3WS/UCIIgCIKYPEhsTxJmQpd7Vavzb7lYLvelKUV+sgw8t7cB3YMjGgErSTJOnRnAkZZe1LX3YVttE+JiGB64rhSxzmDhXIUvHY3dg3AwZikueaR79tpKMAZ0DgwbfKW3rK7A9144oNnHPds+xLqqYqFloVkEVRTp37K6wtDtsK6939BR855tH2Jr9RKlIyYL6cZHdx0Pdm7cdUIRmE4HsLR4Gr77woeGYtGd6yoN4vbnK0vR0efHmmVFir2fK9aBwoxEZKXEoyDDOkqvzjsXRZ/Xbz8Iz+oKXFHoRZ4nAe19fqwqz1XEu1WHSztpPbIEpQBTvej41VvnFlO+dIpqEwRBEMRUQWJ7kjDLoZ6VlYwfXjMbya5YTf7tQytL8bn8NMzJTjGNdIoE63e+MAuuGIch5/ix3XUYGZMNjVTuXzEXff5R5KS6lLSKI629+N4LB5Sou1osHmw6K1w0DIwEsH1/kyJweT4zAEMjGQA41HwWR1t7NZ7V6vQNjllHzbbeYaEdX1FWElyxwZbu/ImBDFlYLNra61cEbFuvH6MBGfe/ckiJfvP0jlsqfPjlG0exfnmJ0kjGDHXeuVnTn9qGLuR5EoTFrVbNh0RpPRt2HsGKslwkxDpQnu9BZ/+wZk75oqMsNwXFWckRO8QQBEEQBBFdSGxPEuocao87DndekY80dzx+8GJQ1P7bn7QR0HtfOoTqpYUoy0vF62sr0dFvjHSKUlP+9U/HUb200JBzfNeVhfjVWyfw7J4GrKsqRnaqC6fODGDz7hPoHhxRUlS6B0dwvK1PE3Xn5KS6MHNakjA6LstAS48fv3or6PaxtDgDjV0Dhq6Gm29bhJExWdjsRZKNjXmcTNysJz0xThM19rjj4B8LgMnQ5HVzD26zFBcuYAGtj7d/NOj+8vCNC/DzHUcsG9io8SbGY2ttI+66stAyTWWibiAOB8PVJVkYDUiG/O9XDzRj9ZJ8xSbyyXfrhX7iBEEQBEGcfxxTPYBPKzxFYOe6Snz/S7PRPxLAj3//kVDUAsHvJRlY85sPwBiwpDBDabvOMUtNkXR9ifgxgKAg7h8O4Ee/O4RNu04o0exNu+twU0UeHlpZihdqm5RteXdKIFiU94udwYI7ddfK+1fMVRq2AOdcT373QbOhq+HBph6hpeEN5Xl49UAzNqwq0+y7NC9oQah+jUer9bnRW96uxzee249btuxBe9+IsjDhCx39PtQpLmZzebytT2kkxAs+rSjwJmL98hI8+W49HtpxBOuqtHNVs6wYrx1sjtgNRJJk1Hf0Y8/JM6jv6IckyWjsHlSEtnouuS/4DeV5YdN5CIIgCII4v1BkexJRt9W+u7LQEPEURYutHD3MHEv0WQJ8Xxyz4rpF09MwIyMR3YMjAGAoNHQ6gIbOIU3BnSwD05LjlW3Urif6cwTM00KcDmD98hJcXZKF0txUQ3dNfR6zOlotyo1WR6HD5UJbzaV63uxY5vFjzV5bicauAaS4YvHU1y/F/oZuDI1K2FrbiLXLiuHzuC33o5kzEztBjzvWsg5g3iXJeH1tJWZkUOoIQRAEQVwoUGR7klFHUHnEk4tafQT0d+83WQo8UcT2oZWlmJuTonntwevn47WDzcr3Jdkpmog1fz3fmwhf+rl9tvT4sbW2EVtWV+C337gMVXOylNd/9dYJbN59Ak++W4+ZGUnYUVOJ56svw46aSsQ6meEcOTwtRH/sqjmZWD4vGzExDhROS9JE8nmqh/o19bmbPRlQR6FF+wg3l/p5sxshdjgYZmYm4ao5WfAkxuGHvzuIoZAIXlGWi8d216GxezDsfjhmHua8GFM/l3IoHWdOdgpmZhrPlSAIgiCIqYPJshz+XRchFRUVcm1t7VQPA/Ud/bh20zvwuOM0tnD53gQ8cF0p+oZHcay1Dy/UNim2elZdGLl9nj4SrH7N53GjsXsQbb1+uOOckGUZDV1Dmlxf9XFE++Sv2+kSaXaOZjnbkXaa1J97R/8w7vjP9wxR6UjzlPXnzefNLBpuhz0nz+Crj+8zvP589WVYUpgxoX28+M0laO8bMeS/cyvB8cwpQRAEQRAThzG2X5blCuHPSGxPLmrB6nHH4aaKPMzKSkZJdgpmZBiF8mS5R5gJ6mhsd77P0e4iYCrgC4+JLASs9sFTavhCajQgKYWhU33uBEEQBPFZhcT2FDNeoXsxcb7P8UKd02gsBC7kxQRBEARBEEZIbBPEeSQaC4ELdTFBEARBEIQRK7FNbiQEEWWsGtWcz30QBEEQBDH1kBsJQRAEQRAEQUwSJLYJgiAIgiAIYpIgsU0QBEEQBEEQkwSJbYIgCIIgCIKYJEhsEwRBEARBEMQkQWKbIAiCIAiCICYJEtsEQRAEQRAEMUmQ2CYIgiAIgiCISYLENkEQBEEQBEFMEiS2CYIgCIIgCGKSILFNEARBEARBEJMEiW2CIAiCIAiCmCSYLMtTPYZJgTHWAaBhig6fAeDMFB37YoXmbHzQvI0PmrfIoTkbHzRv44PmLXJozsZHtOYtX5blaaIffGrF9lTCGKuVZbliqsdxMUFzNj5o3sYHzVvk0JyND5q38UHzFjk0Z+PjfMwbpZEQBEEQBEEQxCRBYpsgCIIgCIIgJgkS25PDlqkewEUIzdn4oHkbHzRvkUNzNj5o3sYHzVvk0JyNj0mfN8rZJgiCIAiCIIhJgiLbBEEQBEEQBDFJkNgmCIIgCIIgiEmCxHYUYYwtZ4wdY4ydYIz9cKrHcyHBGJvOGHuLMXaYMfYxY2xd6PWfMsaaGWMfhv5dq9rmR6G5PMYY+9LUjX7qYIydZowdCs1Nbei1dMbYm4yxutD/ntDrjDG2KTRnBxlj5VM7+qmBMTZbdT99yBjrZYx9m+41I4yx/2SMtTPGPlK9FvH9xRi7I/T+OsbYHVNxLucLkzn7JWPsaGheXmKMpYVeL2CMDanuuV+rtvlc6LN9IjSvbApO57xhMm8RfyY/a39nTeZtq2rOTjPGPgy9TvcbLPXG1P1uk2WZ/kXhHwAngJMACgHEATgAYO5Uj+tC+QcgB0B56OtkAMcBzAXwUwDfE7x/bmgO4wHMCM2tc6rPYwrm7TSADN1rDwP4YejrHwLYEPr6WgB/AMAALAGwb6rHP9X/Qp/LVgD5dK8J52cpgHIAH6lei+j+ApAOoD70vyf0tWeqz+08z9nVAGJCX29QzVmB+n26/bwXmkcWmtdrpvrcpmDeIvpMfhb/zormTffzfwHwY7rfNOdqpjem7HcbRbajx2IAJ2RZrpdleQTA8wCum+IxXTDIstwiy/L7oa/7ABwBkGuxyXUAnpdleViW5VMATiA4x0Rwbp4Jff0MgOtVrz8rB9kLII0xljMF47uQqAJwUpZlq26yn9l7TZbltwF06V6O9P76EoA3ZVnukmW5G8CbAJZP+uCnCNGcybL8hizLY6Fv9wLIs9pHaN5SZFneKwf/qj+Lc/P8qcTkXjPD7DP5mfs7azVvoej0zQB+a7WPz9r9ZqE3pux3G4nt6JEL4BPV902wFpOfWRhjBQAWAdgXemlN6NHNf/LHOqD55MgA3mCM7WeMVYdey5JluSX0dSuArNDXNGdGboX2DxHda+GJ9P6i+dPyDwhGyTgzGGMfMMb+P8ZYZei1XATnifNZnrNIPpN0r2mpBNAmy3Kd6jW631To9MaU/W4jsU2cVxhjSQC2A/i2LMu9AP4DwEwACwG0IPhIjDjHlbIslwO4BsC3GGNL1T8MRSnIv1MAYywOwN8BeCH0Et1rEUL3V2Qwxv4RwBiA/w691ALAJ8vyIgD3APgNYyxlqsZ3AUKfyYnxVWiDCXS/qRDoDYXz/buNxHb0aAYwXfV9Xug1IgRjLBbBG/+/ZVn+HQDIstwmy3JAlmUJwOM49/ie5hOALMvNof/bAbyE4Py08fSQ0P/tobfTnGm5BsD7siy3AXSvRUCk9xfNHwDG2NcBrADwtdAfcoTSIDpDX+9HMN94FoLzo041+UzO2Tg+k3SvhWCMxQC4AcBW/hrdb+cQ6Q1M4e82EtvR4y8AihljM0IRtVsB/H6Kx3TBEMotexLAEVmWN6peV+cUrwTAK65/D+BWxlg8Y2wGgGIECzw+MzDGEhljyfxrBIuwPkJwbnhV9B0AXgl9/XsAt4cqq5cA6FE9Mvssoon60L1mm0jvrz8CuJox5gmlAVwdeu0zA2NsOYAfAPg7WZYHVa9PY4w5Q18XInhv1YfmrZcxtiT0u/F2nJvnzwzj+EzS39lzfAHAUVmWlfQQut+CmOkNTOXvtmhUftI/pQL2WgSrXk8C+MepHs+F9A/AlQg+sjkI4MPQv2sBPAfgUOj13wPIUW3zj6G5PIZPceW0xZwVIlhtfwDAx/yeAuAFsAtAHYA/AUgPvc4A/Co0Z4cAVEz1OUzh3CUC6ASQqnqN7jXjPP0WwUfPowjmI941nvsLwTzlE6F/d071eU3BnJ1AMLeT/277dei9q0Kf3Q8BvA/gK6r9VCAoLk8C2IxQR+dP6z+TeYv4M/lZ+zsrmrfQ608D+KbuvXS/yZZ6Y8p+t1G7doIgCIIgCIKYJCiNhCAIgiAIgiAmCRLbBEEQBEEQBDFJkNgmCIIgCIIgiEmCxDZBEARBEARBTBIktgmCIAiCIAhikiCxTRAEQRAEQRCTBIltgiCIzxiMsf4I3/93jLEfhnnP3zLGXjP52bcZY+5IjkkQBPFpgcQ2QRAEYYksy7+XZfkXE9jFtwGQ2CYI4jMJiW2CIIiLCMZYAWPsKGPsvxljRxhjLzLGUhljxxhjs0Pv+S1j7Bth9vMzxtgBxthexlhW6LVpjLHtjLG/hP79Tej1rzPGNoe+nhna5hBj7EFdlDwpNB4+PsYYqwFwCYC3GGNvTcqkEARBXMCQ2CYIgrj4mA3g32VZLgHQC+AbANYAeJoxdisAjyzLj1tsnwhgryzLCwC8HdoeAB4F8K+yLF+KYOvnJwTbPgrgUVmWSxFsH61mEYJR7LkACgH8jSzLmwD8FcBVsixfFfGZEgRBXOSQ2CYIgrj4+ESW5f8Nff1fAK6UZflNAIcA/ArA3WG2HwHA86v3AygIff0FAJsZYx8C+D2AFMZYkm7bywG8EPr6N7qfvSfLcpMsyxKAD1X7JQiC+MwSM9UDIAiCICJG1n/PGHMAKAEwCMADY9RZzagsy3wfAZz7W+AAsESWZb/6zYwxu+MaVn2t3i9BEMRnFopsEwRBXHz4GGOXh76+DcC7AL4D4Ejo+6cYY7Hj2O8bANbybxhjCwXv2YtgigkA3Gpzv30AkscxHoIgiIseEtsEQRAXH8cAfIsxdgTBKPafEEwd+a4sy+8gmId93zj2WwOggjF2kDF2GMA3Be/5NoB7GGMHARQB6LGx3y0AdlKBJEEQn0XYuSeJBEEQxIUOY6wAwGuyLM+fouO7AQzJsiyHijG/KsvydVMxFoIgiIsByqcjCIIgIuFzCBZRMgBnAfzD1A6HIAjiwoYi2wRBEJ9SGGP7AMTrXl4ty/KhqRgPQRDEZxES2wRBEARBEAQxSVCBJEEQBEEQBEFMEiS2CYIgCIIgCGKSILFNEARBEARBEJMEiW2CIAiCIAiCmCT+f6TnQFfiiykjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "\n",
    "sns.scatterplot(data=mobile_train, x='px_height', y='px_width')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vo7N12X6HFKI"
   },
   "source": [
    "At first, I thought that `batter_power` and `talk_time`(longest time that a single battery charge will last when you are talking) would have a linear relationship since we know it does, but it seems it's not. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RcZQ9Av1GvmF",
    "outputId": "a945043c-e2df-427f-d4ba-fe3fb16cb268"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='battery_power', ylabel='talk_time'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "\n",
    "sns.scatterplot(data=mobile_train, x='battery_power', y='talk_time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7VnNddL2GH7J"
   },
   "source": [
    "#### Exploring Categorical Features\n",
    "\n",
    "Even if all features have numerical values, but there some of them which are categorical. Let's inspect them!! It might be that they are already encoded!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rXPtyLKCGL4n",
    "outputId": "21916bf1-2347-4db8-c281-b4be7a4ca16f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of mobiles that have bluetooth: 990\n",
      "The number of mobiles that don't have bluetooth: 1010\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='blue'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2sY4iS/Ib4IjmKsLg6T11K5LsRmA/4B7jJLJ4N+LXcndFoUGlbp1fPngQJT0/STbrAvwMvcE6SJF0+b0UyfBHgQvRDipl9Tx+ldgV1kEsqNRLlQwfAFwL7GgdRV7iVuB4kuwh6yBWdPm9VEn2C2AfdB52WYwCZwB/1s2FBs3U7ZEMHwpcAkTGSbrVncBxJJl2i0UzdXv4fcX3xO9/VsoziwM1AXwWOECFfpFm6nZLhvcFLgP+yDpK4G4GTifJ7rYOUjYqdSckw734Ezc/DQwbpwnNGuDMqu70WQSVupOS4W2AvwVOBbYyTlN164FzgSuaT9PJHFTqIiTD2+HvzJ4CrDBOUzUbgPOAC0iyjdZhqkClLlIyvD1wJn6XlUHjNGX3NHApcCFJ9qR1mCpRqS0kwzsApwPHo33HZ7sbfyDd1STZiHWYKlKpLSXD/fi9x08ADqF7P2KcAK4HLiLJbrUOU3UqdVkkw68EPth87WycpigPAdcAl5Bkj1qHCYVKXTbJcA9+1n4PcCjhXZ7fi5+VryPJ7rUOEyKVusySYQe8HjgMeCuwP9BrmmnxcvyZZdcB13f7uuwiqNRV4j/3fgu+4AcDu9gG2qyN+LXYtwOrgVtIsidsI3UXlbrKkuGtgdc1X3s3v+4FDBWUYBL4Lb6806/7SLKJgsaXzVCpQ+Pfk++GL/crgO2br5Uzvt8e2I6577aP4U8D3YhfybUOeGQzr/Va3VU+KnW38uVfzkt3SZ0CRkgy/VJUmEotEphuXewgEiyVWiQwKrVIYFRq2YRz7nLn3JPOufuss8jiqdSyOd/Ar2KTClKpZRN5nv8UeNY6hyyNSi0SGJVaJDAqtUhgVGqRwKjUsgnn3NXAz4HXOOcedc59yDqTtE5rv0UCo5laJDAqtUhgVGqRwKjUIoFRqUUCo1KLBEalFgmMSi0SGJVaJDAqtUhgVGqRwKjUIoFRqUUCo1KLBEalFgnM/wE2fIamG8KxgQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displaying number of phones which have or don't have bluetooth \n",
    "blue_count = mobile_train['blue'].value_counts()\n",
    "\n",
    "print(\"The number of mobiles that have bluetooth: {}\\nThe number of mobiles that don't have bluetooth: {}\".format(blue_count[1], blue_count[0]))\n",
    "\n",
    "blue_count.plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EyO0cVxAH2OC",
    "outputId": "3185f864-ca47-450a-bc4c-3fcf70f0b79c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of mobiles that have Wifi: 1014\n",
      "The number of mobiles that don't have Wifi: 986\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='wifi'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displaying number of phones which have or don't have wifi\n",
    "wifi_count = mobile_train['wifi'].value_counts()\n",
    "\n",
    "print(\"The number of mobiles that have Wifi: {}\\nThe number of mobiles that don't have Wifi: {}\".format(wifi_count[1], wifi_count[0]))\n",
    "\n",
    "wifi_count.plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4xyhJjo3HZw0",
    "outputId": "100d4ae7-2b15-4cc4-be9d-18e757c2657f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of mobiles that have dual sim capacity: 1019\n",
      "The number of mobiles that don't have dual sim capacity: 981\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='dual_sim'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displaying number of phones which have or don't have dual simcards\n",
    "sim_count = mobile_train['dual_sim'].value_counts()\n",
    "\n",
    "print(\"The number of mobiles that have dual sim capacity: {}\\nThe number of mobiles that don't have dual sim capacity: {}\".format(sim_count[1], sim_count[0]))\n",
    "\n",
    "sim_count.plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sN_z5adZJjuK",
    "outputId": "744999f7-fc46-456e-964e-9a002819bc10"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of mobiles that have 3G capacity: 1523\n",
      "The number of mobiles that don't have 3G capacity: 477\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='three_g'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displaying number of phones which have or don't have 3g\n",
    "count_3g = mobile_train['three_g'].value_counts()\n",
    "\n",
    "print(\"The number of mobiles that have 3G capacity: {}\\nThe number of mobiles that don't have 3G capacity: {}\".format(count_3g[1], count_3g[0]))\n",
    "\n",
    "count_3g.plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LHKi6sEwKLIr",
    "outputId": "645c4c45-783c-4c8f-b1cf-8f5a74836635"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of mobiles that have 4G capacity: 1043\n",
      "The number of mobiles that don't have 4G capacity: 957\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='four_g'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displaying number of phones which have or don't have 4g\n",
    "\n",
    "count_4g = mobile_train['four_g'].value_counts()\n",
    "print(\"The number of mobiles that have 4G capacity: {}\\nThe number of mobiles that don't have 4G capacity: {}\".format(count_4g[1], count_4g[0]))\n",
    "\n",
    "count_4g.plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OIApO2dJKmJi",
    "outputId": "f0f60df1-f323-4cad-b560-c574100788d2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of mobiles that have touch screens: 1006\n",
      "The number of mobiles that don't have touch screen: 994\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='touch_screen'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAADnCAYAAAAtmKv2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAATYklEQVR4nO3deZAmdX3H8fd3rnU5HChuQWk1iiciiGKiBrWg0BE5PPEgCMGIoGBhuS0Y7TIVHaIpE8EYFI+gxgNIOGwQA1EooyiCsAi6KGSA5RCESnMs7M4z88sf/Sw8O+7M9DPzdH/76efzqnpq5hlm6vdhaj/z6/PXFkJARJplyDuAiPSeii3SQCq2SAOp2CINpGKLNJCKLdJAKrZIA6nYIg2kYos0kIot0kAqtkgDqdgiDaRiizSQii3SQCr2ADKzr5rZvWb2a+8sUg4VezB9HTjIO4SUR8UeQCGEK4EHvHNIeVRskQZSsUUaSMUWaSAVW6SBVOwBZGbfBn4G7GFma83sGO9M0lum5YdFmkcztkgDqdgiDaRiizSQii3SQCPeAaQcUZwOA7sAuwK7zXk9BdgKWAGMtT8OAQGYbX9cD9wL/GHO656Oz++YmpyYqex/SgrTUfEGiOJ0B+ClHa8XkJd6uOShHwVWA9cBv2p/XD01OfFoyePKIlTsPhPF6UpgXzYt8u6uoTY1A6whL/lVQDo1OXGra6IBpGL3gShOtwMOBg4DDgBW+ibq2m+BFPg+8JOpyYmWc57GU7FrKorTpwGHkpf5lZS/WV2VDLiUvOgXT01O/NE5TyOp2DUSxelOwFHAW4B9fNNUokVe8LOAS3QgrndU7BqI4nR/4H3A4cCobxo3d5Gv7PKvU5MTdzhn6XsqtpMoTlcA7wI+BDzfOU6dzAAXAKdPTU782DlL31KxK9Y+EHY88H5gJ+c4dXcd8PGpyYmLvIP0GxW7Iu3TVB8CVgFPdo7Tb34KnDI1OXGFd5B+oWKXLIrTIeBI4O/Ir/qSpbuUvODXegepOxW7RFGcHgj8A/Ai7ywNEoDzgI9NTU6s8Q5TVyp2CaI43RP4DHCgd5YGmwFOB06dmpxY5x2mblTsHoridIx8k/vD6M65qtwCHKP9702p2D0SxemLgG8AL/TOMoAC8EVg1dTkxMPeYepAxV6m9u2Rq4BPkN8CKX6mgGOnJicu8w7iTcVehihO/ww4G3i5dxbZxFnAyVOTEw96B/GiYi9RFKfHkR8g29I7i2zWzcAbB/XIuYrdpfYBsrOAd3tnkUVlwDunJidS7yBV05HbLkRxuj1wOSp1vxgHLozi9BTvIFXTjF1QFKfPJV8o4BneWWRJvgscPSjnvFXsAtpXkH2PfAaQ/nUdcOjU5MRt3kHKpk3xRURx+n7yxQBU6v63F3B1FKeNX8RCxV5AFKefBb6Almlukh2Ay6M4/XPvIGVSsecRxenngZO9c0gpxoEfRnH6au8gZVGxNyOK0zOAD3jnkFJtCVwcxekB3kHKoGLPEcXp6eQrnEjzPQm4oIkzt4rdIYrTTwEneOeQSq0ELori9JXeQXpJp7vaojhdBUx65xA3DwOvmpqc+JV3kF5QsYEoTo8FvuSdQ9zdAbx0anLiHu8gyzXwxY7i9BXAfzO463nLpq4C9p+anFjvHWQ5BnofO4rTXYFzUanlCfvRgK23gS12+y6tc9Ha3vKnjozi9CPeIZZjYIsNnEH+11lkcz4dxekbvEMs1UDuY0dx+l7gTO8cUnsPAS+fmpy40TtItwau2FGc7gdcgdYnk2JuAvbut4NpA7UpHsXpNuT71Sq1FPU84JPeIbo1UMUmX6NsV+8Q0nc+HMVpXy1YOTCb4lGcvoZ8WSORpbgZ2GtqcuJR7yBFDMSM3X7SZd+fmxRXzwb+3jtEUQNRbPLH7jzTO4T0vRPbVyrWXuM3xaM4fQn5ZYLD3lmkEW4B9qz7ooiNnrGjOB0FvoJKLb3zTOBU7xCLaXSxyZc22tM7hDTOSVGc7uIdYiGNLXb7nPUq7xzSSFsAiXeIhTS22OTPqN7GO4Q01tFRnO7hHWI+jSx2+1E8J3rnkEYbAT7lHWI+jSw2+Sb4Vt4hpPEOj+L0Zd4hNqdxxY7idGe0yqhU5zTvAJvTuGIDp5CvPClShb+M4vQg7xBzNarYUZw+FXivdw4ZOLVbbaVRxSY/b73CO4QMnFdHcVqr6yUaU+woTrcAjvLOIQPrJO8AnRpTbOCd6FG34ucdUZzu4B1ioyYV+zjvADLQVlCjLcZGFDuK032BF3vnkIF3rHeAjRpRbOA93gFEgGdFcbq/dwjIL4tblJmtAN4ERJ0/E0JwX+QtitMVwNu9c4i0/TXwY+8QRWfsC4BDgBbwSMerDg4BtvUOIdJ2cHsdAFeFZmxgtxBC7a6uaXubdwCRDk8G9gf+yzNE0Rn7p2b2wlKTLEH7L+MB3jlE5nijd4CixX4FcI2ZrTGz1WZ2g5mtLjNYQX8BbO0dQmSOg70DFN0Uf12pKZaurrlksO0exemLpiYnrvcKUGjGDiHcBjwVeE3783VFf7ZkKrbUlevmeKFymtknyBcv+Gj7S6PAN8sKVUQUp7sBtdvvF2mrf7GBw8iDPgIQQrgL/33buh6lFwHYJ4rTp3gNXrTYG0L+ZIEAYGZblhepMBVb6szID+66KFrs75nZmcA2ZnYscBnw5fJiLSyKUwNe6zW+SEH7eA1c6Kh4COGzZnYA8CCwB/DxEILnCfino6WFpf7qXey2m4EQQrjMzLYws61DCA+VFWwRtVqtQmQebsUuelT8WOBc4Mz2l3YFzi8pUxEqtvSDbaM4fYbHwEX3sY8nPxDwIEAI4XfAjmWFKkDFln7hMmsXLfb6EMKGjW/MbIT2EXInKrb0i1oX+wozOwVY2T6Idg5wUXmx5hfF6ZboIfbSP17iMWjRYq8C7gNuAP4GuBj4WFmhFvEC6nE5q0gRz/cYdNGj4mY2DNwYQngOjueuO+gyUuknO0ZxOjw1OTFT5aCLznwhhBlgjZk9rYI8RdQlh0gRQ8DOVQ9a9Dz2tsCNZvYLOpZECiF4XOhem7WbRQraBbizygGLFvtvS03RHRVb+k3lN4MULfbtwN0hhMcAzGwlsFNpqRa2vdO4Iku1S9UDFj26fA4w2/F+pv01D5qxpd/UttgjnReotD8fKyfSojRjS7+pfFO8aLHvM7PHD5SZ2SHAH8uJNL/27ZrbVT2uyDJVPmMX3cd+H/AtMzuD/AbyO4AjS0s1v22BYYdxRZZji6oHLHo/9i3Afma2Vfv9w6Wmmp+e+CH9qPIngxS9bfNEM3sy+TnsfzKza83swHKjbT6Kw5giy9XNugc9UXQf++gQwoPAgeT7uO8GJktLJdIslRe76IAbZ8rXA2eHEG40M4/Z0/NW0QUNMTszSqs1wsz0CDOtUVrTY7Rao9aaHWGmNUarNcb0zBitmTFrzYwxPTtKa2aM1uwKpmfHbHp2jOnZFbTCKK3ZMZsOK5gOY7TCGNOM0WLUWmGMFo+/p2WjNmMjtBhlxkZpDY0wYyPMDA3b7NAIMzbM7NAws8PDzAwNEYaHmB3u/GiEkfzFMLq5phQzDD0CD1Q6ZtFiX2NmPyRfa+yjZrY1m57XrsSrhq6f2YHs6hqUYMQII+S/v1Fg1Ixh8nKsqPr3IvU2xEzl/yaKFvsYYC/g1hDCOjPbjo6HzZvZ80MIN5aQbxNnj502Dexb9jgiPdaqesCiR8VngWs73t8P3N/xLd8A9u5ttM16tIIxRHqt8mL3ap+qqv1tFVv60WNVD9irYld1UKvyX5BID9xT9YD9dRQ0yQJVH14UWb67qh6wV8XesPi39MztFY4l0gt3Vz1g4RPnZrYrsHvnz4QQrmx/3K/30eZ1O/kRepF+Uc9im9lpwNuAm8jvxYZ8v/rKknItRDO29Jt6Fhs4FNgjhLC+xCxF3eYdQKRLtd3HvhWHO1TmoRlb+k3lR8UXnLHN7HTyTe51wHVmdjnw+KwdQvhgufE2S8WWfvIASVb5lu5im+K/bH+8Briw5CxFqdjST27yGHTBYocQ/g3AzLYEHms/PGDj00G8bna4m3wLovJVKUSW4BqPQYvuY18OrOx4vxK4rPdxCsgvUrl20e8TqYdaF/tJncshtT/3nDGvchxbpBu1LvYjZvb43Vtmtg++N2So2NIPHgF+6zFw0fPYJwHnmNld5Hdy7Ux+wYqXnzmOLVLUdSRZ5QuSQPH7sa82s+cAe7S/tCaEMF1erEUk2V0k42uB3dwyiCzO7VhQ0UtK564hvreZEUI4u4RMRV0FvNlxfJHFuOxfQ/FN8c7liJ4EvJb8r5GKLTK/n3gNXHRT/AOd781sG+A7ZQTqgvazpc5+Q5Ld4jX4Uu/HfoR8xVJPP8fh+WEiBbleqVn0SSAXmdmF7VcKrAH+s9xoi0iyGepzmavIXBd5Dl50H/uzHZ+3gNtCCGtLyNOt84CjvUOIzPFHnHcVC83YIYQryE+0b03+YLwql0JayGVA5h1CZI7U6/z1RkU3xd8K/AJ4C/BW4Odm5n9EOsk2AKl3DJE5XDfDofim+KnAviGEewHMbAfy2fLcsoJ14TzgHd4hRNo2AD/0DlH0qPjQxlK33d/Fz5btB+S3cYrUwQ9Isoe8QxQt5yVmdqmZHWVmR5Fv/l5cXqwuJNk64BLvGCJtX/QOAMWLHYAzgT3bry+Vlmhp6pZHBtOtwKXeIQAshMWfzmNm14YQ9p7ztdUhhD1LS9atZPwm4LneMWSgfYQk+4x3CFhkxjaz48zsBmAPM1vd8fpfYHU1EQv7vHcAGWjrga95h9howRnbzMbJz1t/Gog7/tNDIYR6PUMrGd8CWEueV6Rq3yTJ3u0dYqMFZ+wQQhZCmAohHBFCuK3jVa9Sw8aDaGd5x5CB9S/eATrV5ZRVr5zBE48gEqnK9SRZre42bFaxk+x24HzvGDJwTvMOMFezip37R+8AMlCux39tgj/RvGLnm0QXeMeQgXFKe637WmlesXOryG8vFSnTlSRZPa7AnKOZxU6yNehqNCnfR70DzKeZxc4lwIPeIaSxvk+S/dQ7xHyaW+wkuw+Y9I4hjTQLnOIdYiHNLXbuc8Ad3iGkcc4myW7wDrGQZhc7yR6j5n9Zpe/cA5zsHWIxzS527lvUYEULaYzjSLL6XVI9R6HbNvteMr4rcAO6QUSW5zsk2RHeIYoYhBkbkuxO4ATvGNLX7gU+sOh31cRgFBsgyf4d+J53DOlbJ5BkffPkmcEpdu444G7vENJ3ziXJzvEO0Y3BKnZ+0OMY7xjSV/4AHO8doluDVWyAJLuEfGFGkcVsAN5Ekt276HfWzOAVO3cS+fO1RRZyAkn2P94hlmIwTndtTjK+I/lji3b3jiK19EWS7P3eIZZqUGds2ptXbwDcn9ogtXMFcKJ3iOUY3GIDJNmvgbejddLkCbcBbyHJpr2DLMdgFxto3yhf+2t/pRLrgEPbdwb2NRUbIMn+mZo8c0nctIAjSLLrvIP0gor9hA9Sg+cai4tZ4F0k2YXeQXpFxd4oyVrAm8mfJCqDIwDHkGTf9Q7SSyp2pyTLL0jQY3kHRSC/DfPr3kF6TcWeK8nWA4ehmbvpZsln6kZehahib84T5W7U5pk8bgb4K5KsNk/H7DUVez75ecx3AF/2jiI9tQ54K0n2Te8gZRrcS0q7kYz/HfAx7xiybHcAh5Bkv/IOUjYVu6hk/E3A14GtnJPI0lwFHEaS3eMdpAraFC8qyc4DXgb8zjuKdO0bwP6DUmrQjN29ZHycfOXTCe8osqh8Yf8kq91jbsumGbtbSZYBBwOfJD8PKvWUkV/3PXClBs3Yy5OMHwJ8DS1rXDeXkp+jvtM7iBcVe7mS8Z2AL5BfsSa+HgJOJskG/hSlit0ryfjh5AXf2TvKgPoR8B6S7DbvIHWgfexeSbL/AJ5Lvmku1VlHvpD/a1XqJ2jGLkMyfgD5SqhP947ScJcAHyTJfu8dpG5U7LIk41sCq8hXRN3aN0zj/BL4CEn2I+8gdaVily0Z35684McDK53T9LtbyB+LfA5Jpn+4C1Cxq5KM7wKcChwLjDmn6Tf3kV83cGa/LzJYFRW7asn47sAngCOBYec0dXcP+Vp0nyPJtEx0F1RsL8n4M4H3AUcB2/uGqZ2fAaeTPwxPM/QSqNjekvEVwOHAe4H9fcO4egz4NnAGSXatd5h+p2LXSTL+bPKCHwVs5xumMr8DzgK+QpLd7x2mKVTsOspn8TeS32zyOpq1qR6Aa4DzgfNJsht94zSTil13yfgQsC/5baITwIsBc83UvWny52GdD1xAkq31jdN8Kna/yU+bvR44CHgJELnm2byHyWflX7Rfl5Fk/+eaaMCo2P0uGd8G2Kvj9WLya9ZHK0qwAVhNXuCr26/fkGSzFY0vm6FiN1G+j/488mvVdwR2mue10KWu08B64AFgbcfrzjmf36VTUvWjYg+yZHyE/CKZIfL9diN/ON0GXbLZ31RskQbS/dgiDaRiizSQii3SQCq2/AkzO8jM1pjZ780s9s4j3dPBM9mEmQ0DNwMHkJ/Suho4IoRwk2sw6YpmbJnrpcDvQwi3hhA2AN8BDnHOJF1SsWWuXcmfSrnR2vbXpI+o2CINpGLLXHcCT+14v1v7a9JHVGyZ62rgWWb2dDMbA94OXOicSbo04h1A6iWE0DKzE8gfbDcMfDWEoMUQ+oxOd4k0kDbFRRpIxRZpIBVbpIFUbJEGUrFFGkjFFmkgFVukgVRskQZSsUUaSMUWaSAVW6SBVGyRBlKxRRpIxRZpIBVbpIH+HxUoWKardRqLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displaying number of phones which have or don't have touchscreen\n",
    "\n",
    "touch_scr = mobile_train['touch_screen'].value_counts()\n",
    "\n",
    "print(\"The number of mobiles that have touch screens: {}\\nThe number of mobiles that don't have touch screen: {}\".format(touch_scr[1], touch_scr[0]))\n",
    "\n",
    "touch_scr.plot(kind='pie')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bUWbu7cAM0Oy"
   },
   "source": [
    "There are other features which can be treated as categorical such as number of cores (`n_cores`) or so, but we limited our analysis to the binary features. Often and by convention, categorical features have text values. \n",
    "\n",
    "The only thing standing out from the above categorical features is that many phones have 3G compared to 4G. The value of other features are nearly equal. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can use `pairplots` to display all kinds of relationship between all features. By default, it will al possible plots from numerical features but if you want, you can choose some specific features with `vars` parameter. You can also use `sns.pairplot??` to display its code documentation inside jupyter notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.pairplot??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fcd3b5c40d0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(mobile_train, vars=['n_cores', 'wifi', 'price_range', 'dual_sim', 'ram', 'three_g'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is enough for exploratory data analysis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xOw5dZZJNW8e"
   },
   "source": [
    "<a name='4'></a>\n",
    "\n",
    "## 4. Data Preprocessing\n",
    "\n",
    "In this part, we will prepare the data to be in proper format for ML models. We will do things like feature scaling. We would also had to encode categorical features but they are already encoded. Before we proceed with feature scaling, let's take training labels from training data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "_qpFagOLN3Nf"
   },
   "outputs": [],
   "source": [
    "training_input_data = mobile_train.drop('price_range', axis=1)\n",
    "training_labels = mobile_train['price_range']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "db-juGSNRc6k"
   },
   "source": [
    "#### Feature Scaling \n",
    "\n",
    "This is the only data processing type that we have to take care of here. Let's make a function that can take handle that.\n",
    "\n",
    "We will normalize the data with sklearn `MinMaxScaler` where the numerical values will be scaled to values between 0 and 1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "A8euStSMSBgQ"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "def scale_feats(num_input_data):\n",
    "\n",
    "  \"\"\"\n",
    "  Take input numerical data and return the normalized data\n",
    "\n",
    "  \"\"\"\n",
    "\n",
    "  normalized_data = scaler.fit_transform(num_input_data)\n",
    "\n",
    "  return normalized_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "Q_SjBPNMTTIp"
   },
   "outputs": [],
   "source": [
    "training_final = scale_feats(training_input_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DfPbSNEvTfgN"
   },
   "source": [
    "We are now ready to train our linear classification model. We have come along way, from problem formulation, finding the data, exploring the insights from the data to preparing the data to be in the proper format that will be accepted by ML model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "946dYVSfT2NO"
   },
   "source": [
    "<a name='5'></a>\n",
    "\n",
    "## 5. Creating and Training a Logistic Regression Model\n",
    "\n",
    "We will start by importing `LogisticRegression` models available in Sklearn linear models and then proceed with model training. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GVzJJ1nsT7Ya",
    "outputId": "c509ea6c-ee5c-4627-d856-0fe6f9bf3790"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "log_model = LogisticRegression()\n",
    "log_model.fit(training_final, training_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fiZZUdEOXp3O"
   },
   "source": [
    "Logistic models are simple, they train faster compared to other complex models like ensemble methods or neural networks. Let's now evaluate the model we trained. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4ptUp_zbZ0J4",
    "outputId": "2fefa4d6-800e-4af2-9dba-d15bae466507"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.947"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_model.score(training_final, training_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CE-_DEeqb7_B"
   },
   "source": [
    "Using `log_model.score()`, we can get an accuracy of the model confidence on the training data and labels. 94.7% is not that bad. \n",
    "\n",
    "Let's train another linear classifier called SGD (Stockastic Gradient Descent) classifier on the same dataset. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trying other models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XGINId16kNLb",
    "outputId": "9577b319-f223-44ca-85c0-96d9726aea5e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier()"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier()\n",
    "sgd_clf.fit(training_final, training_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_aHJHLmlk_lM",
    "outputId": "206f496a-007b-4ffc-eec0-5170a390dcff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.819"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.score(training_final, training_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZG-WPu7Zlfw8"
   },
   "source": [
    "Not soo good than a simple logistic classifier. There are many more linear classifiers such as Ridge classifier, but for now let's shift to other complex models a bit. Let's try a decision trees model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "tree_clf = DecisionTreeClassifier()\n",
    "tree_clf.fit(training_final, training_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.score(training_final, training_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ohh so decision tree perfectly fitted the data! But could that be true? This is a signal that the model overfitted. (i.e, it memorized the training data but would likely be poor when tested on the new data). Overfitting is usually caused by using complex models and small training data. As we learn more models, we will be also learning how to regularize them so they can fit the data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FJKmm0_-eS7f"
   },
   "source": [
    "<a name='6'></a>\n",
    "\n",
    "## 6.  Model Evaluation\n",
    "\n",
    "Evaluating classification models is not as simple as for regression models. When you have skewed dataset or imbalances, you can get a high accuracy and you can think the model did well when in fact it didn't. \n",
    "\n",
    "In this part, we are going to learn how to evaluate classification models. But first, let's evaluate the model with cross validation.\n",
    "\n",
    "Here is an idea behind cross validation: We want to divide the training set into different training and validation subsets so that we can iteratively train and validate the models on those subsets. \n",
    "\n",
    "Take an example, if we divide our training data into 10 subsets (commonly known as folds), each subset will take 10% of the whole data. As a result of using cross validation, we will train 10 different models where each model is trained on 9 different subsets and validated on 1 subset or fold, iteratively. By the end of such process, we will have 10 different model scores.\n",
    "\n",
    "Let's run cross validation on the logistic regression model and SGD classifier. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's hide warnings returned by cross validation.\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dgGsY8SdeVC2",
    "outputId": "aee1cf67-b1e0-4335-a371-006f24a1754a"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# cv is the number of subsets/folds \n",
    "\n",
    "log_scores = cross_val_score(log_model, training_final, training_labels,\n",
    "                         cv=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JdXOBa56uP2M",
    "outputId": "ed292f2b-0c3f-495b-b809-99503c22c3ec"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.915, 0.94 , 0.91 , 0.93 , 0.935, 0.94 , 0.92 , 0.925, 0.91 ,\n",
       "       0.92 ])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "njio1nokwGm3",
    "outputId": "c9894d84-cb74-40c1-8f79-2b91716c8eb4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9245000000000001"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "5GbuiLhvuTBq"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "sgd_scores = cross_val_score(sgd_clf, training_final, training_labels,\n",
    "                         cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zVLrqSVSuawc",
    "outputId": "bf2477e4-5a85-47ad-cf04-81484d1c504d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.705, 0.855, 0.76 , 0.735, 0.745, 0.765, 0.79 , 0.765, 0.69 ,\n",
       "       0.745])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Zr5XEY0WwJ0_",
    "outputId": "472b4c1f-59ec-4b72-8d36-0ce39d7915be"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7555000000000001"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "tree_scores = cross_val_score(tree_clf, training_final, training_labels,\n",
    "                         cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.85 , 0.86 , 0.865, 0.81 , 0.855, 0.83 , 0.85 , 0.8  , 0.865,\n",
       "       0.815])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8400000000000001"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eMWdwJUJvITP"
   },
   "source": [
    "As you can see, training both logistic regression, SGD classifier and trees classifier on different subsets of datasets returned different accuracy scores. \n",
    "\n",
    "Even if they both didn't improve much, cross validation can help you to improve your model. But since it depends on the dataset, it's fair to say that it is not guarranted to work well always. \n",
    "\n",
    "If you look at decision tree classifier trained on full data, it had 100% but when evaluated with cross validation, it did poor. We would keep to try other complex models like Random Forests and Support Vector Machines but quite often, they would not improve our results because we have small data. And also, it's better to quickly get few models which work well and spend time improving the data than relying on tuning hyperparameters. \n",
    "\n",
    "So far the only good model we have for our dataset is Logistic regression model. Let's do predictions on it using cross validation again. There can be a confusion that the model saw the data that it is going to be predicted on, but not really! That is the advantage of using cross validation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "# cv is the number of subsets/folds \n",
    "\n",
    "predictions_on_train = cross_val_predict(log_model, training_final, training_labels,\n",
    "                         cv=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Classification Performance Metrics\n",
    "\n",
    "Here are commom classification metrics:\n",
    "\n",
    "* Accuracy \n",
    "* Precision / Recall\n",
    "* F1 Score\n",
    "\n",
    "All of these metrics are easily implemented in Sklearn. \n",
    "\n",
    "Accuracy is the commonly used metric in classification problems. It tells how accurate the model is at making correct predictions. Take an example: If your classifier can only recognize 95 images in 100 images, the accuracy is 95%. Accuracy is not an appropriate metric when the dataset is not balanced. \n",
    "\n",
    "Precision shows the ability of the classifier to recognize positive samples correctly. The recall is the percentage of positive predictions over all positive samples. There is a tradeoff between precision and recall. Increasing precision will decrease recall and vice versa. And for that reason, we combine both of these metrics into another metric called the F1 score. F1 score is the harmonic mean of precision and recall.\n",
    "\n",
    "Both accuracy, precision, and recall can be calculated easily by using a [confusion matrix](https://jeande.tech/how-to-read-a-confusion-matrix). A confusion matrix shows the number of correct and incorrect predictions made by a classifier in all available classes. It's also important to note that a confusion matrix is not a metric, rather a representation of incorrect/correct positive and negative predictions made by a classifier. \n",
    "\n",
    "More intuitively, a confusion matrix is made of 4 main elements: True negatives, false negatives, true positives, and false positives.\n",
    "\n",
    "* True Positives(TP): Number of samples that are correctly classified as positive, and their actual label is positive.\n",
    "\n",
    "* False Positives (FP): Number of samples that are incorrectly classified as positive, when in fact their actual label is negative.\n",
    "\n",
    "* True Negatives (TN): Number of samples that are correctly classified as negative, and their actual label is negative.\n",
    "\n",
    "* False Negatives (FN): Number of samples that are incorrectly classified as negative, when in fact their actual label is positive.\n",
    "\n",
    "Let's find all of the above, starting from confusion matrix.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[488,  12,   0,   0],\n",
       "       [ 31, 441,  28,   0],\n",
       "       [  0,  35, 430,  35],\n",
       "       [  0,   0,  10, 490]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(training_labels, predictions_on_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(training_labels, predictions_on_train)\n",
    "\n",
    "sns.heatmap(cm, square=True, annot=True, fmt='d', cbar=True,\n",
    "                        xticklabels=['0(low cost)', '1(medium cost)', '2(high cost)','3(very high cost)'],\n",
    "                        yticklabels=['0(low cost)', '1(medium cost)', '2(high cost)','3(very high cost)'])\n",
    "plt.ylabel('actual label')\n",
    "plt.xlabel('predicted label');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predicted classes are on the column axis, whereas the actual classes are on the row axis. Remember that the target feature is a price range and it has 4 classes: 0(low cost), 1(medium cost), 2(high cost) and 3(very high cost).\n",
    "\n",
    "So from the confusion matrix above:\n",
    "* The model correctly classified 488 low cost phones as low cost, 12 low cost phones were incorrectly classified as medium cost phones\n",
    "* The model correctly classified 441 medium cost phones as medium phones, incorrectly classified 31 medium cost phones as low cost when in fact they are medium cost, and also incorrectly classified as 28 medium costs phones as high cost when they are not\n",
    "* 430 were correctly classified as high cost, 35 high cost incorrectly classified as medium cost and very high cost. \n",
    "* 490 very high cost phones were correctly classified as very high cost, 10 very high cost phones were incorrectly classified as high cost. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy is how good the model is at making correct predictions over the whole training set. Let's find it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9245"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracy_score(training_labels, predictions_on_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use classification_report to display other metrics. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.98      0.96       500\n",
      "           1       0.90      0.88      0.89       500\n",
      "           2       0.92      0.86      0.89       500\n",
      "           3       0.93      0.98      0.96       500\n",
      "\n",
      "    accuracy                           0.92      2000\n",
      "   macro avg       0.92      0.92      0.92      2000\n",
      "weighted avg       0.92      0.92      0.92      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(training_labels, predictions_on_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it for classifying the price ranges of phones. We have come a long way from data exploration, data processing, creating models and evaluating them. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [BACK TO TOP](#0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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